eLife Assessment

This valuable study combines evolution experiments with molecular and genetic techniques to study how a genetic lesion in MreB that causes rod-shape cells to become spherical, with concomitant deleterious fitness effects, can be rescued by natural selection. The results are convincing, although further improvement of the statistical analyses and figure presentation, and further clarification of the concrete contribution of the paper and how it relates to previous literature, would be welcome.

Reviewer #1 (Public review):

Summary:

The authors performed experimental evolution of MreB mutants that have a slow growing round phenotype and studied the subsequent evolutionary trajectory using analysis tool from molecular biology. It was remarkable and interesting that they found that the original phenotype was not restored (most common in these studies) but that the round phenotype was maintained.

Strengths:

The finding that the round phenotype was maintained during evolution rather than that the original phenotype, rod shape cells, was recovered is interesting. The paper extensively investigates what happens during adaptation with various different techniques. Also the extensive discussion of the findings at the end of the paper is well thought through and insightful.

Weaknesses:

I find there are three general weaknesses<br /> (1) Although the paper states in the abstract that it emphasizes "new knowledge to be gained" it remains unclear what this concretely is. At page 4 they state 3 three research questions, these could be more extensively discussed in the abstract. Also these questions read more like genetics questions while the paper is a lot about cell biological findings.<br /> (2) It is not clear to me from the text what we already know about restoration of MreB loss from suppressors studies (in the literature). Are there supressor screens in the literature and which part of the findings is consistent with suppressor screens and which parts are new knowledge?<br /> (3) The clarity of the figures, captions and data quantification need to be improved.

Reviewer #3 (Public review):

This paper addresses a long-standing problem in microbiology: the evolution of bacterial cell shape. Bacterial cells can take a range of forms, among the most common being rods and spheres. The consensus view is that rods are the ancestral form and spheres the derived form. The molecular machinery governing these different shapes is fairly well understood but the evolutionary drivers responsible for the transition between rods and spheres is not. Enter Yulo et al.'s work. The authors start by noting that deletion of a highly conserved gene called MreB in the Gram-negative bacterium Pseudomonas fluorescens reduces fitness but does not kill the cell (as happens in other species like E. coli and B. subtilis) and causes cells to become spherical rather than their normal rod shape. They then ask whether evolution for 1000 generations restores the rod shape of these cells when propagated in a rich, benign medium.

The answer is no. The evolved lineages recovered fitness by the end of the experiment, growing just as well as the unevolved rod-shaped ancestor, but remained spherical. The authors provide an impressively detailed investigation of the genetic and molecular changes that evolved. Their leading results are:

(1) the loss of fitness associated with MreB deletion causes high variation in cell volume among sibling cells after cell division;<br /> (2) fitness recovery is largely driven by a single, loss-of-function point mutation that evolves within the first ~250 generations that reduces the variability in cell volume among siblings;<br /> (3) the main route to restoring fitness and reducing variability involves loss of function mutations causing a reduction of TPase and peptidoglycan cross-linking, leading to a disorganized cell wall architecture characteristic of spherical cells.

The inferences made in this paper are on the whole well supported by the data. The authors provide a uniquely comprehensive account of how a key genetic change leads to gains in fitness and the spectrum of phenotypes that are impacted and provide insight into the molecular mechanisms underlying models of cell shape.

Suggested improvements and clarifications include:<br /> (1) A schematic of the molecular interactions governing cell wall formation could be useful in the introduction to help orient readers less familiar with the current state of knowledge and key molecular players;<br /> (2) It remains unclear whether corrections for multiple comparisons are needed when more than one construct or strain is compared to the common ancestor, as in Supp Fig 19A (relative PG density of different constructs versus the SBW25 ancestor). The author's response did not clarify matters: was data for the WT obtained independently alongside each each strain/construct (justifying a paired t-test) or was a single set of data for the WT obtained and used to compare against all other strains/constructs (which would demand a correction for multiple comparisons)?<br /> (3) The authors refrain from making strong claims about the nature of selection on cell shape, perhaps because their main interest is the molecular mechanisms responsible. They identify sources of stabilizing selection favouring an intermediate cell size (lack of DNA in small cells and disorganized DNA in large cells). Their interpretation of stabilizing selection in the review is correct and entirely consistent with the mechanistic causes identified here. I think this is valuable and interesting, although I recognize it is not the focus of the paper.

Comments on revisions:

Please further clarify the experimental design and replication for the contrast between mutants and WT to address the issue of multiple comparisons.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

The authors performed experimental evolution of MreB mutants that have a slow-growing round phenotype and studied the subsequent evolutionary trajectory using analysis tools from molecular biology. It was remarkable and interesting that they found that the original phenotype was not restored (most common in these studies) but that the round phenotype was maintained. 

Strengths: 

The finding that the round phenotype was maintained during evolution rather than that the original phenotype, rod-shaped cells, was recovered is interesting. The paper extensively investigates what happens during adaptation with various different techniques. Also, the extensive discussion of the findings at the end of the paper is well thought through and insighXul. 

Weaknesses: 

I find there are three general weaknesses: 

(1) Although the paper states in the abstract that it emphasizes "new knowledge to be gained" it remains unclear what this concretely is. On page 4 they state 3 three research questions, these could be more extensively discussed in the abstract. Also, these questions read more like genetics questions while the paper is a lot about cell biological findings. 

Thank you for drawing attention to the unnecessary and gratuitous nature of the last sentence of the Abstract. We are in agreement. It has been modified, and we have taken  advantage of additional word space to draw attention to the importance of the two competing (testable) hypotheses laid out in the Discussion. 

As to new knowledge, please see the Results and particularly the Discussion. But beyond this, and as recognised by others, there is real value for cell biology in seeing how (and whether) selection can compensate for effects that are deleterious to fitness. The results will very o_en depart from those delivered from, for example, suppressor analyses, or bottom up engineering. 

In the work recounted in our paper, we chose to focus – by way of proof-of principle – on the most commonly observed mutations, namely, those within pbp1A.  But beyond this gene, we detected mutations  in other components of the cell shape / division machinery whose connections are not yet understood and which are the focus of on-going investigation.  

As to the three questions posed at the end of the Introduction, the first concerns whether selection can compensate for deleterious effects of deleting mreB (a question that pertains to evolutionary aspects); the second seeks understanding of genetic factors; the third aims to shed light on the genotype-to-phenotype map (which is where the cell biology comes into play).  Given space restrictions, we cannot see how we could usefully expand, let alone discuss, the three questions raised at the end of the Introduction in restrictive space available in the Abstract.   

(2) It is not clear to me from the text what we already know about the restoration of MreB loss from suppressors studies (in the literature). Are there suppressor screens in the literature and which part of the findings is consistent with suppressor screens and which parts are new knowledge?  

As stated in the Introduction, a previous study with B. subtilis (which harbours three MreB isoforms and where the isoform named “MreB” is essential for growth under normal conditions), suppressors of MreB lethality were found to occur in ponA, a class A penicillin binding protein (Kawai et al., 2009). This led to recognition that MreB plays a role in recruiting Pbp1A to the lateral cell wall. On the other hand, Patel et al. (2020) have shown that deletion of classA PBPs leads to an up-regulation of rod complex activity. Although there is a connection between rod complex and class A PBPs, a further study has shown that the two systems work semi-autonomously (Cho et al., 2016). 

Our work confirms a connection between MreB and Pbp1A, and has shed new light on how this interaction is established by means of natural selection, which targets the integrity of cell wall. Indeed, the Rod complex and class A PBPs have complementary activities in the building of the cell wall with each of the two systems able to compensate for the other in order to maintain cell wall integrity. Please see the major part of the Discussion. In terms of specifics, the connection between mreB and pbp1A (shown by Kawai et al (2009)) is indirect because it is based on extragenic transposon insertions. In our study, the genetic connection is mechanistically demonstrated.  In addition, we capture that the evolutionary dynamics is rapid and we finally enriched understanding of the genotype-to-phenotype map.

(3) The clarity of the figures, captions, and data quantification need to be improved.  

Modifications have been implemented. Please see responses to specific queries listed below.

Reviewer #2 (Public Review): 

Yulo et al. show that deletion of MreB causes reduced fitness in P. fluorescens SBW25 and that this reduction in fitness may be primarily caused by alterations in cell volume. To understand the effect of cell volume on proliferation, they performed an evolution experiment through which they predominantly obtained mutations in pbp1A that decreased cell volume and increased viability. Furthermore, they provide evidence to propose that the pbp1A mutants may have decreased PG cross-linking which might have helped in restoring the fitness by rectifying the disorganised PG synthesis caused by the absence of MreB. Overall this is an interesting study. 

Queries: 

Do the small cells of mreB null background indeed have have no DNA? It is not apparent from the DAPI images presented in Supplementary Figure 17. A more detailed analysis will help to support this claim. 

It is entirely possible that small cells have no DNA, because if cell division is aberrant then division can occur prior to DNA segregation resulting in cells with no DNA. It is clear from microscopic observation that both small and large cells do not divide. It is, however, true, that we are unable to state – given our measures of DNA content – that small cells have no DNA. We have made this clear on page 13, paragraph 2.

What happens to viability and cell morphology when pbp1A is removed in the mreB null background? If it is actually a decrease in pbp1A activity that leads to the rescue, then pbp1A- mreB- cells should have better viability, reduced cell volume and organised PG synthesis. Especially as the PG cross-linking is almost at the same level as the T362 or D484 mutant.  

Please see fitness data in Supp. Fig. 13. Fitness of ∆mreB ∆pbp1A is no different to that caused by a point mutation. Cells remain round.  

What is the status of PG cross-linking in ΔmreB Δpflu4921-4925 (Line 7)? 

This was not analysed as the focus of this experiment was PBPs. A priori, there is no obvious reason to suspect that ∆4921-25 (which lacks oprD) would be affected in PBP activity.

What is the morphology of the cells in Line 2 and Line 5? It may be interesting to see if PG cross-linking and cell wall synthesis is also altered in the cells from these lines. 

The focus of investigation was restricted to L1, L4 and L7. Indeed, it would be interesting to look at the mutants harbouring mutations in :sZ, but this is beyond scope of the present investigation (but is on-going). The morphology of L2 and L5 are shown in Supp. Fig. 9.

The data presented in 4B should be quantified with appropriate input controls. 

Band intensity has now been quantified (see new Supp. Fig .20). The controls are SBW25, SBW25∆pbp1A, SBW25 ∆mreB and SBW25 ∆mreBpbp1A as explained in the paper.

What are the statistical analyses used in 4A and what is the significance value? 

Our oversight. These were reported in Supp. Fig. 19, but should also have been presented in Fig. 4A. Data are means of three biological replicates. The statistical tests are comparisons between each mutant and SBW25, and assessed by paired t-tests.  

A more rigorous statistical analysis indicating the number of replicates should be done throughout. 

We have checked and made additions where necessary and where previously lacking. In particular, details are provided in Fig. 1E, Fig. 4A and Fig. 4B. For Fig. 4C we have produced quantitative measures of heterogeneity in new cell wall insertion. These are reported in Supp. Fig. 21 (and referred to in the text and figure caption) and show that patterns of cell wall insertion in ∆mreB are highly heterogeneous.

Reviewer #3 (Public Review): 

This paper addresses an understudied problem in microbiology: the evolution of bacterial cell shape. Bacterial cells can take a range of forms, among the most common being rods and spheres. The consensus view is that rods are the ancestral form and spheres the derived form. The molecular machinery governing these different shapes is fairly well understood but the evolutionary drivers responsible for the transition between rods and spheres are not. Enter Yulo et al.'s work. The authors start by noting that deletion of a highly conserved gene called MreB in the Gram-negative bacterium Pseudomonas fluorescens reduces fitness but does not kill the cell (as happens in other species like E. coli and B. subtilis) and causes cells to become spherical rather than their normal rod shape. They then ask whether evolution for 1000 generations restores the rod shape of these cells when propagated in a rich, benign medium. 

The answer is no. The evolved lineages recovered fitness by the end of the experiment, growing just as well as the unevolved rod-shaped ancestor, but remained spherical. The authors provide an impressively detailed investigation of the genetic and molecular changes that evolved. Their leading results are: 

(1) The loss of fitness associated with MreB deletion causes high variation in cell volume among sibling cells a_er cell division. 

(2) Fitness recovery is largely driven by a single, loss-of-function point mutation that evolves within the first ~250 generations that reduces the variability in cell volume among siblings. 

(3) The main route to restoring fitness and reducing variability involves loss of function mutations causing a reduction of TPase and peptidoglycan cross-linking, leading to a disorganized cell wall architecture characteristic of spherical cells. 

The inferences made in this paper are on the whole well supported by the data. The authors provide a uniquely comprehensive account of how a key genetic change leads to gains in fitness and the spectrum of phenotypes that are impacted and provide insight into the molecular mechanisms underlying models of cell shape. 

Suggested improvements and clarifications include: 

(1) A schematic of the molecular interactions governing cell wall formation could be useful in the introduction to help orient readers less familiar with the current state of knowledge and key molecular players. 

We understand that this would be desirable, but there are numerous recent reviews with detailed schematics that we think the interested reader would be better consulting. These are referenced in the text.

(2) More detail on the bioinformatics approaches to assembling genomes and identifying the key compensatory mutations are needed, particularly in the methods section. This whole subject remains something of an art, with many different tools used. Specifying these tools, and the parameter sesngs used, will improve transparency and reproducibility, should it be needed. 

We overlooked providing this detail, which has now been corrected by provision of more information in the Materials and Methods. In short we used Breseq, the clonal option, with default parameters. Additional analyses were conducted using Genieous. The BreSeq output files are provided https://doi.org/10.17617/3.CU5SX1 (which include all read data).

(3) Corrections for multiple comparisons should be used and reported whenever more than one construct or strain is compared to the common ancestor, as in Supplementary Figure 19A (relative PG density of different constructs versus the SBW25 ancestor). 

The data presented in Supp Fig 19A (and Fig 4A) do not involve multiple comparisons. In each instance the comparison is between SBW25 and each of the different mutants. A paired t-test is thus appropriate.

(4) The authors refrain from making strong claims about the nature of selection on cell shape, perhaps because their main interest is the molecular mechanisms responsible. However, I think more can be said on the evolutionary side, along two lines. First, they have good evidence that cell volume is a trait under strong stabilizing selection, with cells of intermediate volume having the highest fitness. This is notable because there are rather few examples of stabilizing selection where the underlying mechanisms responsible are so well characterized. Second, this paper succeeds in providing an explanation for how spherical cells can readily evolve from a rod-shaped ancestor but leaves open how rods evolved in the first place. Can the authors speculate as to how the complex, coordinated system leading to rods first evolved? Or why not all cells have lost rod shape and become spherical, if it is so easy to achieve? These are important evolutionary questions that remain unaddressed. The manuscript could be improved by at least flagging these as unanswered questions deserving of further attention. 

These are interesting points, but our capacity to comment is entirely speculative. Nonetheless, we have added an additional paragraph to the Discussion that expresses an opinion that has yet to receive attention:

“Given the complexity of the cell wall synthesis machinery that defines rod-shape in bacteria, it is hard to imagine how rods could have evolved prior to cocci. However, the cylindrical shape offers a number of advantages. For a given biomass (or cell volume), shape determines surface area of the cell envelope, which is the smallest surface area associated with the spherical shape. As shape sets the surface/volume ratio, it also determines the ratio between supply (proportional to the surface) and demand (proportional to cell volume). From this point of view, it is more efficient to be cylindrical (Young 2006). This also holds for surface attachment and biofilm formation (Young 2006). But above all, for growing cells, the ratio between supply and demand is constant in rod shaped bacteria, whereas it decreases for cocci. This requires that spherical cells evolve complex regulatory networks capable of maintaining the correct concentration of cellular proteins despite changes in surface/volume ratio. From this point of view, rod-shaped bacteria offer opportunities to develop unsophisticated regulatory networks.”

why not all cells have lost rod shape and become spherical.

Please see Kevin Young’s 2006 review on the adaptive significance of cell shape

The value of this paper stems both from the insight it provides on the underlying molecular model for cell shape and from what it reveals about some key features of the evolutionary process. The paper, as it currently stands, provides more on which to chew for the molecular side than the evolutionary side. It provides valuable insights into the molecular architecture of how cells grow and what governs their shape. The evolutionary phenomena emphasized by the authors - the importance of loss-of-function mutations in driving rapid compensatory fitness gains and that multiple genetic and molecular routes to high fitness are o_en available, even in the relatively short time frame of a few hundred generations - are wellunderstood phenomena and so arguably of less broad interest. The more compelling evolutionary questions concern the nature and cause of stabilizing selection (in this case cell volume) and the evolution of complexity. The paper misses an opportunity to highlight the former and, while claiming to shed light on the latter, provides rather little useful insight. 

Thank you for these thoughts and comments. However, we disagree that the experimental results are an overlooked opportunity to discuss stabilising selection. Stabilising selection occurs when selection favours a particular phenotype causing a reduction in underpinning population-level genetic diversity. This is not happening when selection acts on SBW25 ∆mreB leading to a restoration of fitness. Driving the response are biophysical factors, primarily the critical need to balance elongation rate with rate of septation. This occurs without any change in underlying genetic diversity.  

Recommendations for the authors:  

Reviewer 1 (Recommendations for the Authors): 

Hereby my suggestion for improvement of the quantification of the data, the figures, and the text. 

-  p 14, what is the unit of elongation rate?  

At first mention we have made clear that the unit is given in minutes^-1

-  p 14, please give an error bar for both p=0.85 and f=0.77, to be able to conclude they are different 

Error on the probability p is estimated at the 95% confidence interval by the formula:1.96 , where N is the total number of cells. This has been added in the paragraph p »probability » of the Image Analysis section in the Material and Methods. 

We also added errors on p measurement in the main text.

-  p 14, all the % differences need an errorbar 

The error bars and means are given in Fig 3C and 3D.

-  Figure 1B adds units to compactness, and what does it represent? Is the cell size the estimated volume (that is mentioned in the caption)? Shouldn't the datapoints have error bars? 

Compactness is defined in the “Image Analysis” section of the Material and Methods. It is a dimensionless parameter. The distribution of individual cell shapes / sizes are depicted in Fig 1B. Error does arise from segmentation, but the degree of variance (few pixels) is much smaller than the representations of individual cells shown.

-  Figure 1C caption, are the 50.000 cells? 

Correct. Figure caption has been altered.

-  Figure 1D, first the elongation rate is described as a volume per minute, but now, looking at the units it is a rate, how is it normalized? 

Elongation rate is explained in the Materials and Methods (see the image analysis section) and is not volume per minute. It is dV/dt = r*V (the unit of r is min^-1). Page 9 includes specific mention of the unit of r.

-  Figure 1E, how many cells (n) per replicate? 

Our apologies. We have corrected the figure caption that now reads:

“Proportion of live cells in ancestral SBW25 (black bar) and ΔmreB (grey bar) based on LIVE/DEAD BacLight Bacterial Viability Kit protocol. Cells were pelleted at 2,000 x g for 2 minutes to preserve ΔmreB cell integrity. Error bars are means and standard deviation of three biological replicates (n>100).”

-  Figure 1G, how does this compare to the wildtype 

The volume for wild type SBW25 is 3.27µm^3 (within the “white zone”). This is mentioned in the text.

-  Figure 2B, is this really volume, not size? And can you add microscopy images? 

The x-axis is volume (see Materials and Methods, subsection image analysis). Images are available in Supp. Fig. 9.

-  Figure 3A what does L1, L4 and L7 refer too? Is it correct that these same lines are picked for WT and delta_mreB 

Thank you for pointing this out. This was an earlier nomenclature. It was shorthand for the mutants that are specified everywhere else by genotype and has now been corrected. 

-  Figure 3c: either way write out p, so which probability, or you need a simple cartoon that is plotted. 

The value p is the probability to proceed to the next generation and is explained in Materials and Methods  subsection image analysis.  We feel this is intuitive and does not require a cartoon. We nonetheless added a sentence to the Materials and Methods to aid clarity.

-  Figure 4B can you add a ladder to the gel? 

No ladder was included, but the controls provide all the necessary information. The band corresponding to PBP1A is defined by presence in SBW25, but absence in SBW25 ∆pbp1A.

-  Figure 4c, can you improve the quantification of these images? How were these selected and how well do they represent the community? 

We apologise for the lack of quantitative description for data presented in Fig 4C. This has now been corrected. In brief, we measured the intensity of fluorescent signal from between 10 and 14 cells and computed the mean and standard deviation of pixel intensity for each cell. To rule out possible artifacts associated with variation of the mean intensity, we calculated the ratio of the standard deviation divided by the square root of the mean. These data reveal heterogeneity in cell wall synthesis and provide strong statistical support for the claim that cell wall synthesis in ∆mreB is significantly more heterogeneous than the control. The data are provided in new Supp. Fig. 21. 

Minor comments: 

-  It would be interesting if the findings of this experimental evolution study could be related to comparative studies (if these have ever been executed).  

Little is possible, but Hendrickson and Yulo published a portion of the originally posted preprint separately. We include a citation to that paper. 

-  p 13, halfway through the page, the second paragraph lacks a conclusion, why do we care about DNA content? 

It is a minor observation that was included by way of providing a complete description of cell phenotype.  

-  p 17, "suggesting that ... loss-of-function", I do no not understand what this is based upon. 

We show that the fitness of a pbp1A deletion is indistinguishable from the fitness of one of the pbp1A point mutants. This fact establishes that the point mutation had the same effects as a gene deletion thus supporting the claim that the point mutations identified during the course of the selection experiment decrease (or destroy) PBP1A function.

-  p 25, at the top of the page: do you have a reference for the statement that a disorganized cell wall architecture is suited to the topology of spherical cells? 

The statement is a conclusion that comes from our reasoning. It stems from the fact that it is impossible to entirely map the surface of a sphere with parallel strands.

eLife Assessment

Gil Ávila et al. evaluated the aperiodic component in the medial prefrontal cortex using resting-state EEG recordings from 149 individuals with chronic pain and 115 healthy participants. The authors present compelling evidence that the aperiodic component of the EEG does not differentiate between those with chronic pain and healthy individuals. The study was well-designed and rigorously conducted, and the clear and conclusive results provide important insights that can guide future research in the field of pain neuroscience.

Reviewer #1 (Public review):

Summary:

In this study, Avila et al. tested the hypothesis that chronic pain states are associated with changes in excitability of the medial prefrontal cortex (mPFC). The authors used the slope of the aperiodic component of the EEG power spectrum (= the aperiodic exponent) as a novel, non-invasive proxy for the cortical excitation-inhibition ratio. They performed source localization to estimate the EEG signals generated specifically by the mPFC. By pooling resting-state EEG recordings from three existing datasets, the authors were able to compare the aperiodic exponent in the mPFC and across the whole brain (at all modeled cortical sources) between 149 chronic pain patients and 115 healthy controls. Additionally, they assessed the relationship between the aperiodic exponent and pain intensity reported by the patients. To account for heterogeneity in pain etiology, the analysis was also performed separately for two patient subgroups with different chronic pain conditions (chronic back pain and chronic widespread pain). The study found robust evidence against differences in the aperiodic exponent in the mPFC between people with chronic pain and healthy participants, and no correlation was observed between the aperiodic exponent and pain intensity. These findings were consistent across different patient subgroups and were corroborated by the whole-brain analysis.

Strengths:

The study is based on sound scientific reasoning and rigorously employs suitable methods to test the hypothesis. It follows a pre-registered protocol, which greatly increases the transparency and, consequently, the credibility of the reported results. In addition to the planned steps, the authors used a multiverse analysis to ensure the robustness of the results across different methodological choices. I find this particularly interesting, as the EEG aperiodic exponent has only recently been linked to network excitability, and the most appropriate methods for its extraction and analysis are still being determined. The methods are clearly and comprehensively described, making this paper very useful for researchers planning similar studies. The results are convincing, supported by informative figures, and the lack of the expected difference in mPFC excitability between the tested groups is thoroughly and constructively discussed.

Weaknesses:

Firstly, to augment the sample size, the authors pooled data recorded by different researchers using different experimental protocols. This inevitably increases sample variability and may limit the availability of certain measures, as was the case here with the reports of pain intensity in the patient group. Secondly, the analysis heavily relies on the estimation of cortical sources, an approach that may yield imprecise results, especially when default conduction models, source models, and electrode coordinates are used (as was the case here).

Comments on revisions:

The authors satisfactorily revised the manuscript and responded to previous questions and suggestions. I have no further comments.

Reviewer #2 (Public review):

Summary:

This study evaluated the aperiodic component in the medial prefrontal cortex (mPFC) using resting-state EEG recordings from 149 individuals with chronic pain and 115 healthy participants. The findings showed no significant differences in the aperiodic component of the mPFC between the two groups, nor was there any correlation between the aperiodic component and pain intensity. These results were consistent across various chronic pain subtypes and were corroborated by whole-brain analyses. The study's robustness was further reinforced by preregistration and multiverse analyses, which accounted for a wide range of methodological choices.

Strengths:

This study was rigorously conducted, yielding clear and conclusive results. Furthermore, it adhered to stringent open and reproducible science practices, including preregistration, blinded data analysis, and Bayesian hypothesis testing. All data and code have been made openly available, underscoring the study's commitment to transparency and reproducibility.

Weaknesses:

The aperiodic exponent of the EEG power spectrum is often regarded as an indicator of the excitatory/inhibitory (E/I) balance. However, this measure may not be the most accurate or optimal for quantifying E/I balance, a limitation that the authors might consider addressing in the future.

Comments on revisions:

All my comments have been well addressed.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews

Reviewer #1: 

Summary:

In this study, Avila et al. tested the hypothesis that chronic pain states are associated with changes in the excitability of the medial prefrontal cortex (mPFC). The authors used the slope of the aperiodic component of the EEG power spectrum (= the aperiodic exponent) as a novel, non-invasive proxy for the cortical excitation-inhibition ratio. They performed source localization to estimate the EEG signals generated specifically by the mPFC. By pooling resting-state EEG recordings from three existing datasets, the authors were able to compare the aperiodic exponent in the mPFC and across the whole brain (at all modeled cortical sources) between 149 chronic pain patients and 115 healthy controls. Additionally, they assessed the relationship between the aperiodic exponent and pain intensity reported by the patients. To account for heterogeneity in pain etiology, the analysis was also performed separately for two patient subgroups with different chronic pain conditions (chronic back pain and chronic widespread pain). The study found robust evidence against differences in the aperiodic exponent in the mPFC between people with chronic pain and healthy participants, and no correlation was observed between the aperiodic exponent and pain intensity. These findings were consistent across different patient subgroups and were corroborated by the whole-brain analysis.

Strengths:

The study is based on sound scientific reasoning and rigorously employs suitable methods to test the hypothesis. It follows a pre-registered protocol, which greatly increases the transparency and, consequently, the credibility of the reported results. In addition to the planned steps, the authors used a multiverse analysis to ensure the robustness of the results across different methodological choices. I find this particularly interesting, as the EEG aperiodic exponent has only recently been linked to network excitability, and the most appropriate methods for its extraction and analysis are still being determined. The methods are clearly and comprehensively described, making this paper very useful for researchers planning similar studies. The results are convincing, and supported by informative figures, and the lack of the expected difference in mPFC excitability between the tested groups is thoroughly and constructively discussed.

We are grateful for the appreciation of the strengths of our study.  

Weaknesses:

Firstly, although I appreciate the relatively large sample size, pooling data recorded by different researchers using different experimental protocols inevitably increases sample variability and may limit the availability of certain measures, as was the case here with the reports of pain intensity in the patient group. Secondly, the analysis heavily relies on the estimation of cortical sources, an approach that offers many advantages but may yield imprecise results, especially when default conduction models, source models, and electrode coordinates are used. In my opinion, this point should be discussed as well.

We agree that the heterogeneous sample of people with chronic pain increases variability and limits the availability of clinical measures. We further agree on the limitations of source space analysis. Therefore, we have added these limitations to the discussion section.

Reviewer #2: 

Summary:

This study evaluated the aperiodic component in the medial prefrontal cortex (mPFC) using restingstate EEG recordings from 149 individuals with chronic pain and 115 healthy participants. The findings showed no significant differences in the aperiodic component of the mPFC between the two groups, nor was there any correlation between the aperiodic component and pain intensity. These results were consistent across various chronic pain subtypes and were corroborated by whole-brain analyses. The study's robustness was further reinforced by preregistration and multiverse analyses, which accounted for a wide range of methodological choices.

Strengths:

This study was rigorously conducted, yielding clear and conclusive results. Furthermore, it adhered to stringent open and reproducible science practices, including preregistration, blinded data analysis, and Bayesian hypothesis testing. All data and code have been made openly available, underscoring the study's commitment to transparency and reproducibility.

We appreciate the appraisal of the strengths of our study, highlighting our efforts in open and reproducible science practices.

Weaknesses:

The aperiodic exponent of the EEG power spectrum is often regarded as an indicator of the excitatory/inhibitory (E/I) balance. However, this measure may not be the most accurate or optimal for quantifying E/I balance, a limitation that the authors might consider addressing in the future.

We are grateful for this suggestion and fully agree that the aperiodic component of the power spectrum is not necessarily the most optimal and accurate measure for quantifying E/I balance. We have now included this limitation in the discussion section.

Recommendations for the authors

Reviewer #1: 

(1) In the Results section, it might be helpful to provide the mean values of the aperiodic exponent (before age correction) for all tested groups and subgroups. As this measure is still not widely used, providing these values would allow readers to better understand the normal range of the aperiodic exponent.

We have added the mean values of the aperiodic exponent and their standard deviation (before age correction) to the manuscript's results section (page 6 and 11).

(2) When reporting the aperiodic exponent across all cortical sources (Q3), I think it would be useful to include the raw values in Figure 6 in the main text rather than in the Supplementary Materials. At a glance, these plots seem to suggest that the aperiodic exponent differs between groups in the occipital and parietal regions, even though no tests were significant after correcting for multiple comparisons. Maybe this observation also deserves a mention in the text and possibly in the Discussion..?

We have moved the report on the aperiodic exponent across all cortical sources from the Supplementary Material to the main text. It is now Fig. 7 of the main manuscript. Moreover, we agree that the plots suggest group differences in certain brain regions. However, according to our rigorous open and reproducible science practices and pre-registration, we prefer not to speculate on these non-significant findings. 

(3) In the Methods section, when describing the participants, the authors state that "Gender was balanced across both groups...". It might be better to avoid referring to the datasets as "balanced," considering that the sample includes almost twice as many females as males.

We have replaced the misleading statement with the more precise statement that ”the gender ratio of both groups was similar.”

(4) In the Methods section, when describing the source localization, I find it slightly confusing that the authors first mention the anterior cingulate cortex as a possible label included in the mPFC cortical parcels but then state that the version of the cortical atlas used did not contain such a label. It might be simpler not to mention the cingulate cortex at all.

We have deleted the misleading sentence from the manuscript.  

Reviewer #2: 

(1) The aperiodic exponent of the EEG power spectrum is often considered an indicator of the excitatory/inhibitory (E/I) balance, but this measure can be susceptible to artifacts. It is important to acknowledge this limitation and consider exploring alternative measures to quantify the E/I ratio in future studies.

We are grateful for this suggestion and fully agree that the aperiodic component of the power spectrum is not necessarily the most optimal and accurate measure for quantifying E/I balance. We have now included this limitation in the discussion section.

(2) The study assumed a linear relationship between the E/I ratio (represented by the aperiodic exponent of the EEG power spectrum) and chronic pain. However, this assumption may not hold true in all cases, and this point could be discussed in the study.

We fully agree that the relationship between the E/I ratio and chronic pain might not be a linear one and have added this point to the discussion section.

(3) The aperiodic component was characterized in eyes-closed resting-state EEG recordings, although EEG data were collected in both eyes-closed and eyes-open conditions. The authors could also consider assessing the aperiodic component from EEG data with eyes open.

We thank the reviewer for this suggestion. We have focused our analysis on eyes-closed recordings since these recordings are usually less contaminated by artifacts than eyes-open recordings. Moreover, in our current datasets, some participants were missing eyes-open recordings. We agree that performing similar analyses for the eyes-open recordings would also be interesting. However, adding these analyses would double the amount of data included in the manuscript, which would likely overload it. We have, therefore, now included a statement to the discussion that future studies should also analyze eyes-open EEG recordings.  

(4) The EEG power spectrum was calculated from signals after source reconstruction, a crucial step for targeting specific brain regions. However, this process can introduce potential signal distortions, such as variations in source waveforms depending on different regularization parameters. To ensure the robustness of the results, the authors could perform the same analysis at the sensor level, for example, using signals recorded at Fz.

We agree on the potential shortcomings and limitations of source space analysis and have added this limitation to the discussion section.

(5) It would be beneficial to present the raw EEG power spectrum averaged across subjects for each condition, along with the scalp distribution of the aperiodic exponent. This would enhance readers' understanding of the study and help demonstrate the quality of the data.

We are grateful for this suggestion and added the power spectrum for each condition and the scalp distribution of the aperiodic exponent to the Supplementary Material.

(6) Linear regression models were used to control for the influence of age on aperiodic exponents and pain intensity ratings. However, it is unclear why other relevant variables, such as gender and medication use, were not considered.

We agree that the aperiodic exponent might be influenced by gender and medication. As these analyses had not been included in our pre-registered analysis plan, we have not performed them. Moreover, although we agree that gender might have an impact, we have not found any evidence for this so far. Regarding medication, we fully agree that medication can influence the measure. However, medication was very heterogeneous, including drugs with fundamentally different mechanisms of action. Thus, we do not see a robust way to appropriately analyze these effects with sufficient statistical power. We have now added this important point to the discussion section.

(7) The authors may consider addressing or discussing the impact of inter-individual variability on the negative results, particularly given that the data were derived from multiple experiments.

We agree that the heterogeneous sample of people with chronic pain increases variability and limits the availability of clinical measures. We have added this limitation to the discussion.

eLife Assessment

This important study enhances our understanding of the foraging behaviour of aerial insectivorous birds. Using solid methodology, the authors have collected extensive data on bird movements and prey availability, which in turn provide support for the main claim of the study. The work will be of broad interest to behavioural ecologists.

Reviewer #1 (Public review):

This study tests whether Little Swifts exhibit optimal foraging, which the data seem to indicate is the case. This is unsurprising as most animals would be expected to optimize the energy income : expenditure ratio, however it hasn't been explicitly quantified before the way it was in this manuscript.

The major strength of this work is the sheer volume of tracking data and the accuracy of those data. The ATLAS tracking system really enhanced this study and allowed for pinpoint monitoring of the tracked birds. These data could be used to ask and answer many questions beyond just the one tested here.

The major weakness of this work lies in the sampling of insect prey abundance at a single point on the landscape, 6.5 km from the colony. This sampling then requires the authors to work under the assumption that prey abundance is simultaneously even across the study region. It may be fair to say that prey populations might be correlated over space but are not equal. It is uncertain whether other aspects of the prey data are problematic. For example, the radar only samples insects at 50m or higher from the ground - how often do Little Swifts forage under 50m high?

The finding that Little Swifts forage optimally is indeed supported by the data, notwithstanding some of the shortcomings in the prey abundance data. The authors achieved their aims and the results support their conclusions.

At its centre, this work adds to our understanding of Little Swift foraging and extends to a greater understanding of aerial insectivores in general. While unsurprising that Little Swifts act as optimal foragers, it is good to have quantified this and show that the population declines observed in so many aerial insectivores are not necessarily a function of inflexible foraging habits. Further, the methods used in this research have great potential for other work. For example, the ATLAS system poses some real advantages and an exciting challenge to existing systems, like MOTUS. The radar that was used to quantify prey abundance also presents exciting possibilities if multiple units could be deployed to get a more spatially-explicit view.

To improve the context of this work, it is worth noting that this research goes into much further depth than any previous studies on a similar topic in several flycatcher and swallow species. A further justification is posited that this research is needed due to dramatic insect population declines, however, the magnitude and extent of such declines are fiercely debated in the literature.

Reviewer #2 (Public review):

Summary:

Bloch et al. studied the relationships between aerial foragers (lesser swifts) tracked using an automated radio telemetry system (Atlas) and their prey (flying insects) monitored using a small vertical-looking radar (BirdScan MR1). The aim of the study was to check whether swifts optimise their foraging according to the abundance of their prey. The results provide evidence that small swifts can increase their foraging rate when aerial insect abundance is high, but found no correlation between insect abundance and flight energy expenditure.

Key points:

This study fills gaps in fundamental knowledge of prey-predator dynamics in the air. It describes the coincidence between the abundance of flying insects and the characteristics derived from monitoring individual swifts.

Weaknesses:

The paper uses assumptions largely derived from optimal foraging theory, but mixes up the form of natural selection: parental energy, parental survival (predation risk), nestling foraging and reproductive success. The results are partly inconsistent, and confounding factors (e.g., the brooding phase versus the nestling phase) remained ignored. In conclusion, the analyses performed are insufficient to rigorously assess whether lesser swifts are optimising their foraging beyond making shorter foraging trips.<br /> The filters applied to the monitoring data are necessary but may strongly influence the characteristics derived based on maximum or mean values. Sensitivity tests or the use of characteristics that are less dependent on extreme values could provide more robust results.

Author response:

The following is the authors’ response to the original reviews.

eLife Assessment

This valuable work advances our understanding of the foraging behaviour of aerial insectivorous birds. Its major strength is the large volume of tracking data and the accuracy of those data. However, the evidence supporting the main claim of optimal foraging is incomplete.

We deeply appreciate the thoughtful review provided by the reviewers, including their valuable insights and meticulous attention to detail. Each comment has been thoroughly evaluated, leading to substantial improvements in the manuscript. Your constructive critique has been instrumental in refining our research and rectifying any oversights. We are confident that the revised article will make a substantial contribution to ecological research, particularly in advancing our understanding of foraging theories and the behaviors of aerial insectivores.

Public Reviews:

Reviewer #1 (Public Review):

This study tests whether Little Swifts exhibit optimal foraging, which the data seem to indicate is the case. This is unsurprising as most animals would be expected to optimize the energy income: expenditure ratio; however, it hasn't been explicitly quantified before the way it was in this manuscript.

The major strength of this work is the sheer volume of tracking data and the accuracy of those data. The ATLAS tracking system really enhanced this study and allowed for pinpoint monitoring of the tracked birds. These data could be used to ask and answer many questions beyond just the one tested here.

The major weakness of this work lies in the sampling of insect prey abundance at a single point on the landscape, 6.5 km from the colony. This sampling then requires the authors to work under the assumption that prey abundance is simultaneously even across the study region - an assumption that is certainly untrue. The authors recognize this problem and say that sampling in a spatially explicit way was beyond their scope, which I understand, but then at other times try to present this assumption as not being a problem, which it very much is.

Further, it is uncertain whether other aspects of the prey data are problematic. For example, the radar only samples insects at 50 m or higher from the ground - how often do Little Swifts forage under 50 m high?

Another example might be that the phrases "high abundance" and "low abundance" are often used in the manuscript, but never defined.

It may be fair to say that prey populations might be correlated over space but are not equal. It is this unknown degree of spatial correlation that lends confidence to the findings in the Results. As such, the finding that Little Swifts forage optimally is indeed supported by the data, notwithstanding some of the shortcomings in the prey abundance data. The authors achieved their aims and the results support their conclusions.

Thanks for this comment.

The basic assumption of this paper is that the abundance of insects bioflow in the airspace is correlated in space and varies over time. This has been demonstrated by different studies, see for example Bell et al. (Bell, J. R., Aralimarad, P., Lim, K. S., & Chapman, J. W. (2013). Predicting insect migration density and speed in the daytime convective boundary layer. PloS one, 8(1), e54202) in which positive correlation in insect bioflow is demonstrated between different sites that are more than 100 km away in Southern England. Given the much closer proximity of the colony and the radar site, as well as the large foraging distance of the swifts that often forage in the vicinity of the radar and beyond it, it is reasonable to assume that the radar was able to successfully capture between-day variation in the abundance of flying insects in the airspace, which is highly relevant for the foraging swifts. This is likely because meteorological variables such as temperature and wind, which tend to vary over a synoptic-system scale of several hundred kilometers, significantly influence the abundance of aerial insects. Furthermore, the direction of insect flight that has been recorded by the radar points to an overall south-north directionality of the insects during the period of the study (Werber et al. Under Review: Werber, Y., Chapman, J. W., Reynolds, D. R. and Sapir, N. Active navigation and meteorological selectivity drive patterns of mass intercontinental insect migration through the Levant). Hence, it is reasonable to assume that since the colony is positioned approximately 6.5 km south of the radar site, the radar is able to reliable estimate the between-day variation in aerial insect abundance experienced by the foraging swifts. Importantly, this between-day variation is very high, and detailed information regarding this variation is provided in the paper.  We thank the reviewer for the comments on the wording and have corrected it accordingly so that it is explicitly stated that the spatial distribution of the flying insects is indeed not uniform, but is expected to be simultaneously affected by environmental variables creating spatially correlated bioflow of aerial insects.

The term "high abundance" or "low abundance" is relative to the variable being examined but throughout the manuscript we did not use these terms to describe an absolute amount or a certain threshold but rather to describe the ecological circumstances experienced by the birds on different days that substantially varied in abundance of insect recorded by the radar. However, we have improved the wording of the text so that it is now clear that we refer to relative  and not to absolute values.

At its centre, this work adds to our understanding of Little Swift foraging and extends to a greater understanding of aerial insectivores in general. While unsurprising that Little Swifts act as optimal foragers, it is good to have quantified this and show that the population declines observed in so many aerial insectivores are not necessarily a function of inflexible foraging habits. Further, the methods used in this research have great potential for other work. For example, the ATLAS system poses some real advantages and an exciting challenge to existing systems, like MOTUS. The radar that was used to quantify prey abundance also presents exciting possibilities if multiple units could be deployed to get a more spatially-explicit view.

To improve the context of this work, it is worth noting that the authors suggest that this work is important because it has never been done before for an aerial insectivore; however, that justification is untrue as it has been assessed in several flycatcher and swallow species. A further justification is that this research is needed due to dramatic insect population declines, but the magnitude and extent of such declines are fiercely debated in the literature. Perhaps these justifications are unnecessary, and the work can more simply be couched as just a test of optimality theory.

We appreciate the reviewer's helpful comment. A flycatcher is indeed an aerial insect eater, but its foraging strategy is very different from that of swifts. A comparison with the foraging strategy of the swallow is much more relevant. However, the methods used to quantify bird movement in the airspace in previous articles limited the ability to examine the optimal foraging theory in detail. Following the comment, we revised the text to better describe the uniqueness of our research. Further, since we studied insectivores, it is important to provide a broad context to potentially significant threats to the birds, albeit being debatable

Reviewer #2 (Public Review):

Summary:

Bloch et al. investigate the relationships between aerial foragers (little swifts) tracked with an automated radio-telemetry system (Atlas) and their prey (flying insects) monitored with a small-scale vertical-looking radar device (BirdScan MR1). The aim of the study was to test whether little swifts optimise their foraging with the abundance of their prey. However, the results provided little evidence of optimal foraging behaviour.

Strengths:

This study addresses fundamental knowledge gaps on the prey-predator dynamics in the airspace. It describes the coincidence between the abundance of flying insects and features derived from tracking individual swifts.

Weaknesses:

The article uses hypotheses broadly derived from optimal foraging theory, but mixes the form of natural selection: parental energetics, parental survival (predation risks), nestling foraging, and breeding success.

While this study explores additional behavioral theories alongside optimal foraging theory, its findings unequivocally support the latter. The highly statistically significant observed reduction in flight distance from the breeding colony in elation to increasing insect abundance (supporting predictions 1 and 2) coupled with an increased rate of colony visits (supporting prediction 5) demonstrate the Little Swifts' adeptness at optimizing their aerial foraging behavior. This behavior manifests in an enhanced frequency of visits to the breeding colony, underscoring their food provisioning maximization.

Results are partly incoherent (e.g., "Thus, even when the birds foraged close to the colony under optimal conditions, the shorter traveling distance is not thought to not confer lower flight-related energetic expenditure because more return trips were made.", L285-287),

Thanks for the comment. We have corrected this sentence.

and confounding factors (e.g., brooding vs. nestling phase) are ignored.

The breeding stage may indeed affect food provisioning properties but this factor is not confounded since insect abundance, and the consequent changes in bird foraging properties, fluctuated between sequential days while brooding and nestling phases take place over a period of several weeks, each. Further, despite the possible influence of breeding stages on bird behavior, variability in reproductive stages is expected among pairs in a breeding colony occupying dozens of pairs, despite some coordination in nesting initiation. Practically, the narrow and concealed nest openings hindered direct observation of the nests, posing challenges in determining the precise reproductive stage of each pair. Anyway, we added a short description of the dense colony structure to the Methods section.

Some limits are clearly recognised by the authors (L329 and ff).

See above the response about the distribution of insects in space.

To illustrate potential confounding effects, the daily flight duration (Prediction 4) should decrease with prey abundance, but how far does the daily flight duration coincide with departure and arrival at sunrise and sunset (note that day length increases between March and May), respectively, and how much do parents vary in the duration of nest attendance during the day across chick ages?

We added the following explanation to the Methods section:

To standardize the effect of day length on daily foraging duration, we calculated and subtracted the day length from the total daily foraging time (Day duration - Daily foraging duration = Net foraging duration). The resulting data represent the daily foraging duration in relation to sunrise and sunset, independent of day length.

To conclude, insufficient analyses are performed to rigorously assess whether little swifts optimize their foraging.

We disagree. See our responses above.

Filters applied on tracking data are necessary but may strongly influence derived features based on maximum or mean values. Providing sensitivity tests or using features less dependent on extreme values may provide more robust results.

Thank you for highlighting the importance of considering the impact of data filtering on derived features. In our analysis, we employed rigorous filtering methods to emphasize central data tendencies while mitigating the influence of extreme values. These methods, validated through consultation with experts in tracking data analysis, follow established practices in the literature. Detailed descriptions of our filtering procedures can be found in the Methods section, with citations to relevant published studies.

Radar insect monitoring is incomplete and strongly size-dependent. What is the favourite prey size of swifts? How does it match with BirdScan MR1 monitoring capability?

We added an explanation to the Methods section to address this comment:

The Radar Cross Section (RCS) quantifies the reflectivity of a target, serving as a proxy for size by representing the cross-sectional area of a sphere with identical reflectivity to water, whose diameter equals the target's body length. Recent findings indicate that the BirdScan MR1 radar can detect insects with an RCS as low as 3 mm², enabling the detection of insects with body lengths as small as 2 mm. These capabilities make the radar suitable for locating the primary prey of swifts, which typically range in size from 1 to 16 mm.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Lines 53-59 - major run-on sentence

Thanks for the comment. Done.

Line 133 - describe better. Attached where? Were feathers clipped or removed?

Thanks for the comment. Done.

Line 153 - shouldn't be a new paragraph

Done.

Line 157 - justify choosing four 

To ensure a robust analysis of swifts' behavior relative to food abundance across multiple individuals simultaneously, we opted to exclude data from instances where only 3 tags were active. This decision was motivated by the fact that these instances accounted for only 2.9% of the data, and their exclusion minimally impacted overall data volume while enhancing data quality. In contrast, instances with 4 tags, comprising 16.2% of the data, provided substantial insights. Omitting these instances would have resulted in significant data loss. Thus, setting a threshold of 4 simultaneous tags represents a balance between maintaining adequate data quantity and ensuring high data quality for meaningful analysis.

It took me a long time to determine whether the average and maximum flight distance was actual or Euclidean. It was only in the Results that I grasped it was actual. Define up front in the Methods.

Thanks for the comment. Done.

In my public review, I mention that optimal foraging has been assessed in other aerial insectivores. Here are some of the papers I was referring to:

• Davies (1977) Prey selection and the search strategy of the spotted flycatcher (Muscicapa striata): A field study on optimal foraging. Animal Behaviour 25: 1016-1022.

• Lifjeld & Slagsvold (1988) Effects of energy costs on the optimal diet: an experiment with pied flycatchers Ficedula hypoleuca feeding nestlings. Ornis Scandinavica 19: 111-118.

• Quinney & Ankney (1985) Prey size selection by tree swallows. Auk 102: 245-250.

• Turner (1982) Optimal foraging by the swallow (Hirundo rustica, L): Prey size selection. Animal Behaviour 30:862-872.

Lastly, in terms of the work not being spatially-explicit, I do note that in lines 323-324 you acknowledge that prey populations can be patchy, then ten lines later, you provide citations to say that patchiness is not a problem because of spatial correlations. This is a bit overly dismissive, in my view, and to suggest (lines 336-337) that "patches of high insect concentration...might not exist at all" is certainly incorrect (and misleading). I do note the valiant attempt to address the spatial shortcoming in the remainder of the paragraph - although addressing it does not make the problem go away.

Thanks for the comment.

We revised the text to make it more coherent.

Reviewer #2 (Recommendations For The Authors):

L161: typo > missing space in 'meanof'

Corrected.

L192-193: Did the authors use the timing of sunrise and sunset to determine daytime?

Yes. The daytime was calculated in relation to sunrise and sunset.

Did the authors calculate the MTR from sunrise to sunset, or averaging the hourly MTR?

If using hourly MTR, specify the criteria to assign an hourly MTR to daytime when sunset/sunrise is happening during that hour.

A simplified terminology for "Average daily insect MTR" might be useful, in particular for the result section (insect MTR).

Average daily insect MTR is calculated for a fixed period from 5 am to 8 pm local time. An explanation has been added to the Methods section, and the terminology in the text has been simplified as suggested

Note that the 'M' of MTR stands for migration, which may not be appropriate in this context, and simply using "insect traffic rate" may be a better terminology.

Thanks for the comment. The 'M' of MTR can also stand for movement, as the insects detected by the radar move in the airspace. This is how this term has been defined in the paper (e.g. in line 23 of the Summary section). Therefore, we did not change the terminology to “insect traffic rate”, which is a term not used in other studies.

Considering the large number of predictions (10!), it would be appropriate to list them in the results (e.g., "on the daily average flight distance from the breeding colony (Prediction 3)").

We added prediction numbers to the Results and the Discussion.

Note that the terminology varies; e.g., in the introduction "overall daily flight distance" (L75), in the results "average length of the daily flight route" (L236), and further confusion with "daily average flight distance from the breeding colony" (L232).

Thanks for the comment. fixed.

The terminology - average daily 'air/flight' distance (L74-76) - needs clarification.

Done.

Results: Use only a relevant and consistent number of decimals to report on the effect size and p-values.

Done.

The authors are citing non-peer-reviewed publications:

21. Bloch I, Troupin D, Sapir N. Movement and parental care characteristics during the nesting season of 468 the Little Swift (Apus affinis) [Poster presentation]. 12th European Ornithologists' Union Congress. Cluj Napoca, Romania. 2019.

62. Zaugg S, Schmid B, Liechti F. Ensemble approach for automated classification of radar echoes into functional bird sub-types. In: Radar Aeroecology. 2017. p. 1. doi:10.13140/RG.2.2.23354.80326

It is acceptable to cite non-peer-reviewed sources if they have a significant contribution to the background of the article without a critical impact on the core of the research.

eLife Assessment

This important study describes two findings: first, that TEAD is subject to turnover by the ubiquitin-proteasome system involving RNF146 and Parylation, and second, the development of a pan-TEAD heterobifunctional degrader that is used to inhibit growth of a YAP-dependent cancer cell line and to characterize TEAD binding sites in the genome. Convincing evidence supports the development and specificity of the degrader. This article will be of relevance to cancer biologists and scientists interested in proteostasis, cellular signaling, and post-translation modification of proteins.

Reviewer #1 (Public review):

Summary:

In the first half of this study, Pham et al. investigate the regulation of TEAD via ubiquitination and PARylation, identifying an E3 ubiquitin ligase, RNF146, as a negative regulator of TEAD activity through an siRNA screen of ubiquitin-related genes in MCF7 cells. The study also finds that depletion of PARP1 reduced TEAD4 ubiquitination levels, suggesting a certain relationship between TEAD4 PARylation and ubiquitination which was also explored through an interesting D70A mutation. Pham et al. subsequently tested this regulation in D. melanogaster by introducing Hpo loss-of-function mutations and rescuing the overgrowth phenotype through RNF146 overexpression.

In the second half of this study, Pham et al. designed and assayed several potential TEAD degraders with a heterobifunctional design, which they term TEAD-CIDE. Compounds D and E were found to effectively degrade pan-TEAD, an effect which could be disrupted by treatment with TEAD lipid pocket binders, proteasome inhibitors, or E1 inhibitors, demonstrating that the TEAD-CIDEs operate in a proteasome-dependent manner. These TEAD-CIDEs could reduce cell proliferation in OVCAR-8, a YAP deficient cell line, but not SK-N-FI, a Hippo pathway independent cell line. Finally, this study also utilizes ATAC-seq on Compound D to identify reductions in chromatin accessibility at the regions enriched for TEAD DNA binding motifs.

Strengths:

The study provides compelling evidence that the E3 ubiquitin ligase RNF146 is a novel negative regulator of TEAD activity. The authors convincingly delineate the mechanism through multiple techniques and approaches. The authors also describe the development of heterobifunctional pan-degraders of TEAD, that could serve as valuable reagents to more deeply study TEAD biology.

Weaknesses:

The scope of this study is extremely broad. The first half of the paper highlights the mechanisms underlying TEAD degradation; however, the connection to the second half of the paper on small molecule degraders of TEAD is jarring, and it seems as though two separate stories were combined into this single massive study. In my opinion, the study would be stronger if it chose to focus on only one of these topics and instead went deeper.

Additionally, the figure clarity needs to be substantially improved, as readability and interpretation was difficult in many panels. Lastly, there are numerous typos and poor grammar throughout the text that need to be addressed.

Comments on revisions:

The authors have addressed most of our critiques. The manuscript has improved significantly, particularly in the clarity of the figures and the flow of the text. The findings of this study contribute valuable insights into TEAD biology in cancer and provide useful resources for further research into TEAD.

However, as noted by other reviewers, the manuscript still feels somewhat disjointed, despite the attempt to connect the two parts on RNF146-mediated TEAD degradation and the development of TEAD degraders. Certain data inconsistencies and technical limitations may have made some aspects of the data hard to interpret accurately and could benefit from further clarification.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In the first half of this study, Pham et al. investigate the regulation of TEAD via ubiquitination and PARylation, identifying an E3 ubiquitin ligase, RNF146, as a negative regulator of TEAD activity through an siRNA screen of ubiquitin-related genes in MCF7 cells. The study also finds that depletion of PARP1 reduced TEAD4 ubiquitination levels, suggesting a certain relationship between TEAD4 PARylation and ubiquitination which was also explored through an interesting D70A mutation. Pham et al. subsequently tested this regulation in D. melanogaster by introducing Hpo loss-of-function mutations and rescuing the overgrowth phenotype through RNF146 overexpression.

In the second half of this study, Pham et al. designed and assayed several potential TEAD degraders with a heterobifunctional design, which they term TEAD-CIDE. Compounds D and E were found to effectively degrade pan-TEAD, an effect which could be disrupted by treatment with TEAD lipid pocket binders, proteasome inhibitors, or E1 inhibitors, demonstrating that the TEAD-CIDEs operate in a proteasome-dependent manner. These TEAD-CIDEs could reduce cell proliferation in OVCAR-8, a YAP-deficient cell line, but not SK-N-FI, a Hippo pathway independent cell line. Finally, this study also utilizes ATAC-seq on Compound D to identify reductions in chromatin accessibility at the regions enriched for TEAD DNA binding motifs.

Strengths:

The study provides compelling evidence that the E3 ubiquitin ligase RNF146 is a novel negative regulator of TEAD activity. The authors convincingly delineate the mechanism through multiple techniques and approaches. The authors also describe the development of heterobifunctional pan-degraders of TEAD, which could serve as valuable reagents to more deeply study TEAD biology.

Weaknesses:

The scope of this study is extremely broad. The first half of the paper highlights the mechanisms underlying TEAD degradation; however, the connection to the second half of the paper on small molecule degraders of TEAD is jarring, and it seems as though two separate stories were combined into this single massive study. In my opinion, the study would be stronger if it chose to focus on only one of these topics and instead went deeper.

We thank the reviewer for the thoughtful feedback. In our mind, the two parts of the paper are inherently related as they both focus on proteasome-mediated degradation of TEADs. We first demonstrated that TEAD can be turned over by the ubiquitin proteasome system under endogenous conditions and identified a PARylation-dependent E3 ligase RNF146 as a major regulator of TEAD stability. Intriguingly, we observed that the four TEAD paralogs show different levels of polyubiquitination with some of them being highly stable in cells. These observations raised the question of whether the activity of the ubiquitin-proteasome system could be further enhanced pharmacologically to effectively target TEADs. We then tackled this question by providing a proof-of-concept demonstration of engineered heterobifunctional protein degraders can effectively degrade TEADs in cells and can be exploited as a therapeutic strategy for treating Hippo-dependent cancers.

Additionally, the figure clarity needs to be substantially improved, as readability and interpretation were difficult in many panels. Lastly, there are numerous typos and poor grammar throughout the text that need to be addressed.

We appreciate the suggestions from the reviewer and have updated the figures with high resolution images. We also corrected typos and grammatical errors in the text.

Reviewer #2 (Public Review):

The paper is made of two parts. One deals with RNF146, the other with the development of compounds that may cause TEAD degradation. The two parts are rather unrelated to each other.

The main limit of this work is the lack of evidence that TEAD factors are in fact regulated by the proteasome and ubiquitylation under endogenous conditions. Also lacking is the demonstration that TEADs are labile proteins to the extent that such quantitative regulation at the level of stability can impact on YAP-TAZ biology. Without these two elements, the relevance and physiological significance of all these data is lacking.

As for the development of new inhibitors of TEAD, this is potentially very interesting but underdeveloped in this manuscript. Irrespectively, if TEAD is stable, these molecules are likely lead compounds of interest. If TEAD is unstable, as entertained in the first part of the paper, then these molecules are likely marginal.

We thank the reviewer for evaluating our manuscript. As the reviewer pointed out, the paper aimed to address 1) whether TEAD is being regulated by the proteasome and ubiquitination under endogenous conditions, and 2) whether TEAD can be inhibited through pharmacologically-induced degradation. First, we demonstrated that TEAD is ubiquitinated in cells and mapped the lysine residues that are poly-ubiquitinated (Fig. 1). Next, we identified RNF146 as a major E3 ligase that ubiquitinates TEADs and reduces their stability. Third, we show that RNF146-mediated TEAD ubiquitination is functionally important: RNF146 suppresses TEAD activity, and RNF146 genetically interacts with Hippo pathway components in fruit flies. Furthermore, as we showed in Fig. S2H, RNF-146 does not affect TEAD1 and TEAD4 to the same extent. Across all four cell lines evaluated, TEAD1 is more stable than TEAD4, raising the question of whether more consistent degradation of different TEAD paralogues could be achieved. To this end, we demonstrated that while the TEAD family of proteins is labile under endogenous conditions, more complete degradation of the TEAD proteins could be achieved using a heterobifunctional CRBN degrader. We further characterized these TEAD degraders in a series of cellular and genomic assays to demonstrate their cellular activity, selectivity, and inhibitory effects against YAP/TAZ target genes. We believe these degrader compounds would be of great interest to the Hippo community. We have revised the main text to clarify these points.

Here are a few other specific observations:

(1) The effect of MG is shown in a convoluted way, by MS. What about endogenous TEAD protein stability?

We thank the reviewer for the question. The MS experiment shown in Figure 1 is a standard KGG experiment, where we used MS to map ubiquitination sites on TEADs. The graphical representation of the process is included in Fig. 1C, and the details of the procedure are included in the Methods section. Fig. 1D shows the different KGG peptides detected with or without MG-132 treatment. Fig. 1E shows the quantified abundance of each of the peptides across the four conditions indicated at the bottom of the plot. Regarding endogenous TEAD stability, ​​we conducted cycloheximide chase experiments to assess the stability of endogenously expressed TEAD isoforms upon RNF146 knockdown (Fig. S2G and S2H). Using isoform-specific antibodies, we demonstrated that siRNF146 significantly stabilized TEAD4 in multiple cell lines, including H226, PATU-8902, Detroit-562, and OVCAR-8 (Fig. S2G, S2H, and S2I), supporting the notion that RNF146 is a negative regulator of TEAD stability. Notably, the effect of siRNF146 on TEAD1 stability was less pronounced, and TEAD1 is more stable than TEAD4 across all four cell lines. These results are consistent with the lower level of ubiquitination of TEAD1 (Fig. 1A) and are corroborated by various biochemical, molecular, and genetic characterizations (Fig. 3A-C and S3E).

(2) The relevance of siRNF on YAP target genes of Fig.2D is not statistically significant.

We thank the reviewer for this comment. We have now removed the statistically significant claim.

(3) All assays are with protein overexpression and Ub-laddering

We thank the reviewer for the comment. To examine the ubiquitination level of TEAD proteins, we adopted an in vivo ubiquitination assay as described in our Materials and Methods section. To our knowledge, this assay is very standard in the ubiquitination field. Furthermore, as mentioned above, we have included in our revised manuscript cycloheximide chase experiments to assess the stability of endogenously expressed TEAD isoforms upon RNF146 knockdown (Fig. S2G and S2H). In addition to the overexpression system, we also assessed endogenously expressed TEAD using isoform-specific antibodies. We demonstrated that siRNF146 firmly stabilized TEAD4 in multiple cell lines, including H226, PATU-8902, Detroit-562, and OVCAR-8 (Fig. S2G with quantification and t-test), supporting the notion that RNF146 is a negative regulator of TEAD stability.

(4) An inconsistency exists on the only biological validation (only by overexpression) on the fly eye size. RNF gain in Fig4C is doing the opposite of what is expected from what is portrayed here as a YAP/TEAD inhibitor: RNF gain is shown to INCREASE eye size, phenocopying a Hippo loss of function phenotype. According to the model proposed, RNF addition should reduce eye size. The authors stated that " This is in contrast to the anti-growth effect of RNF-146 in the Hpo loss-of-function background and indicates RNF146 may regulate other genes/pathways controlling eye sizes besides its role as a negative regulator of Sd/yki activity". This raises questions on what the authors are really studying: why, according to the authors, these caveats should occur on the controls, and not when they study Hpo mutants?

We thank the reviewer for the comment. We acknowledge the complexity of the fly phenotype compared to tumor growth. TEAD (Sd) isn’t the only substrate of RNF146 in the fly. For instance, RNF146 is known to positively regulate Wnt signaling by degrading Axin. Previous studies have shown that activation of the Wnt signaling pathway by removal of the negative regulator Axin from clones of cells results in an overgrowth phenotype (Legent and Treisman, 2008). The overgrowth phenotype that we observed with overexpressing RNF146 only, therefore, likely is due to the role of RNF146 in regulating other signaling pathways. Importantly, we showed that upon Hippo loss of function, overexpression of RNF146 can rescue the Hippo overgrowth phenotype (Fig 4B). This differential outcome of RNF146 expression in wildtype versus Hippo-deficient flies indicates that the genetic interactions between RNF146 and Hippo pathway components altered the phenotypic outcome, and the phenotype we get with RNF146 overexpression in a Hippo loss of function background is not simply due to additive effects of functional loss of either component alone.

Complementary to these overexpression data, we showed that knockdown of RNF146 increased the eye size further (Fig. S4A, B) in Hippo loss of function background, further supporting the role of RNF146 as a negative regulator of the overall pro-growth signals induced by yki upon Hippo loss of function.

(5) The role of TEAD inactivation on YAP function is already well known. Disappointingly, no prior literature is cited. In any case, this is a mere control.

We thank the reviewer for the suggestion. We have cited several published reviews that touch upon this aspect of the TEAD-YAP function, including Calses et al., 2019; Dey et al., 2020; Halder and Johnson, 2011; Wang et al., 2018. We are open to your suggestions on additional citations.

(6) The second part of the paper on the Development and Screening of pan-TEAD lipid pocket degraders is interesting but unconnected to the above. The degradation pathway it involves has nothing to do with the enzyme described in the first figures.

We thank the reviewer for the comment. We acknowledge that our paper broadly covers two aspects. We believe that they are inherently connected as they both address ubiquitin/proteasome-mediated TEAD degradation and the functional consequences of TEAD degradation. Given the increasing interest in targeting TEAD/YAP/TAZ in cancers, we think the pharmacological approaches to enhance TEAD degradation using orthogonal E3 ligases provide an important toolbox to understand how this pathway can be regulated under both physiological and pathological conditions. While RNF146 appears to be a major E3 ligase responsible for TEAD turnover under physiological conditions, we showed that the four TEAD paralogs have different poly-ubiquitination levels (Fig. 1A), and are differentially labile in cells (Fig. S2G-I). These observations raised the question of whether the activity of the ubiquitination-proteasome system could be further enhanced to allow more complete removal of TEADs. To this end, we demonstrated that E3 ligases that do not regulate TEAD under endogenous conditions can be leveraged pharmacologically to achieve deep TEAD degradation, thus providing a proof of concept that TEADs can be targeted simultaneously using such approaches. Finally, in addition to establishing the basic biological concept linking RNF146 to TEAD degradation, the compounds we engineered will serve as valuable chemical tools for future studies of TEAD biology and the Hippo pathway in cancers and beyond.

(7) The role of CIDE on YAP accessibility to Chromatin is superficially executed. Key controls are missing along with the connection with mechanisms and prior knowledge of TEAD, YAP, chromatin, and other TEAD inhibitors, just to mention a few.

We used ATAC-seq to assess chromatin accessibility comparing cells treated with DMSO and two different concentrations of compound D. We acknowledge there are small molecule inhibitors of TEADs that can modulate accessibility of YAP binding sites. Potential mechanistic differences between TEAD degraders versus TEAD small molecule inhibitions will be a future area of investigation.

(8) The physiological relevance and the mechanistic interpretation of what should be in the ATAC seq in ovcar cells is missing.

We showed in Fig. 7A-D the dose response of OVCAR cells to the TEAD degraders. As evident from those experiments, TEAD degraders inhibit the proliferation of OVCAR cells as expected from their dependencies on the TEAD/YAP/TAZ transcription complex. In the ATAC-seq experiment, we showed that the canonical TEAD/YAP/TAZ target genes ANKRD1 and CCN1 have reduced chromatin accessibility at their promoter/enhancer regions (Fig. 8C). By unbiased motif and pathway analyses, we show that TEAD binding sites and YAP signatures are most significantly downregulated in OVCAR-8 cells (Fig. 8D-E). These results are incorporated into the results section of the manuscript.

Reviewer #3 (Public Review):

Summary

Pham, Pahuja, Hagenbeek, et al. have conducted a comprehensive range of assays to biochemically and genetically determine TEAD degradation through RNF146 ubiquitination. Additionally, they designed a PROTAC protein degrader system to regulate the Hippo pathway through TEAD degradation. Overall, the data appears robust. However, the manuscript lacks detailed methodological descriptions, which should be addressed and improved before publication. For instance, the methods used to analyze the K48 ubiquitination site on TEAD and the gene expression analysis of Hippo Signaling are unclear. Furthermore, the multiple proteomics, RNA-seq, and ATAC-seq data must be made publicly available upon publication to ensure reproducibility. Most of the main figures are of low resolution, which needs addressing.

We thank the reviewer for evaluating our manuscript. All of the data will be uploaded to public databases. We apologize for the low figure resolution and have updated the figures in the revised manuscript. We also expanded the methods section with more details.

Strengths:

- A broad range of assays was used to robustly determine the role of RNF146 in TEAD degradation.

- Development of novel PROTAC for degrading TEAD.

Weaknesses:

- An orthogonal approach is needed (e.g., PARP1 inhibitor) to demonstrate PARP1's dependency in TEAD ubiquitination.

We thank the reviewer for the suggestion. We had attempted to assess the effect of PARP inhibitors (including veliparib and olaparib) on TEAD ubiquitination, but the data is relatively complex to interpret. Besides inhibiting PARP1/2 catalytic activities, these PARP inhibitors also trap PARP on chromatin. Hence, these inhibitors could induce other cellular changes in addition to inhibiting the catalytic activities of PARP1/2. Given these potential pitfalls, we decided not to include these inconclusive data. Even though the experiments with PARP inhibitors were inconclusive, our study supports that TEAD2 and TEAD4 are PARylated in cells using an anti-PAR antibody (Fig. 3B). Furthermore, we show that mutation of the D70 PARsylation site to alanine greatly abolished TEAD4 ubiquitination in cells, suggesting PARylation is important for TEAD4 ubiquitination. In addition, PARP1 depletion by siRNA and CRISPR guide RNA reduced TEAD2 and TEAD4 ubiquitination levels, indicating PARP1 is one of the PARPs responsible for TEAD PARylation in cells.

- The data from Table 2 is unclear in illustrating the association of identified K48 ubiquitination with TEAD4, especially since the experiments were presumably to be conducted on whole cell lysates with KGG enrichment. This raises the possibility that the K48 ubiquitination could originate from other proteins. Alternatively, if the authors performed immunoprecipitation on TEAD followed by mass spectrometry, this should be explicitly described in the text and materials and methods section.

We thank the reviewer for this question. The experiment was an IP-mass spectrometry study in a TEAD4 amplified cell line model (PATU-8902) after IP with a pan-TEAD antibody. Here, we observed K48 ubiquitin and other ubiquitin linkages as shown in the Supplementary Table S2 of the original submission. Although it is possible that the IP wash steps could be more stringent, we did enrich for TEAD protein prior to mass spectrometry. While the ubiquitin linkage signals may come mainly from TEAD protein (mainly TEAD4), we recognized that some signals may come from other proteins. Given the caveat, we have now removed the table from our paper and updated the text accordingly.

- Figure 2D: The methodology for measuring the Hippo signature is unclear, as is the case for Figures 7E and F regarding the analysis of Hippo target genes.

We apologize for the lack of clarification. In short, we previously developed the Hippo signature using machine learning and chemogenomics as described previously (Pham et al. Cancer Discovery 2021). In the revised version of the manuscript, we added the methodology for measuring the Hippo signature and cited our previous publication where we developed the Hippo signature.

- Figure S3F requires quantification with additional replicates for validation.

We thank the reviewer for the suggestion. We added the quantification for the blot and indicated the replication in the figure legend. Note that Figure S3F is now S3G.

- There is a misleading claim in the discussion stating "TEAD PARylation by PAR-family members (Figure 3)"; however, the demonstration is only for PARP1, which should be corrected.

We apologize for the statement. We observed both PARP1 and PARP9 in our TEAD IP-mass spec (now Figure S3E), which suggest both PARP-family members could be invovled. Nonetheless, we primarily focus on PARP1, which is widely expressed aross cell line models and present in higher abundance. Thus, our study only experimentally validated PARP1's role in regulating TEAD.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

General comments:

(1) Please provide a smoother transition and well-defined connection between the first and second parts of the manuscript. The manuscript reads as two papers that were combined into one, without much attempt to disguise the fact.

We thank the reviewer for the suggestion. We have added a transition paragraph to smoothen the transition. We acknowledge that our paper broadly covers two aspects. However, they both touch upon TEAD ubiquitination and degradation. In the first part of the manuscript, we described TEAD biology and showed that TEADs are post-translationally modified and subsequently regulated through PARylation-dependent RNF146-mediated ubiquitination. In the second part, we highlighted our abilities to leverage the PROTAC system for degrading such labile oncogenic proteins like TEADs. In addition to the biological concept, the compounds we engineered will serve as valuable chemical tools for future studies of TEAD biology and the Hippo pathway in cancers and beyond.

(2) To confirm the proteasome mechanism of action, viability assays should be conducted with a CRBN KO.

We thank the reviewer for the comment. In Figure 6E, we measured TEAD protein levels under CRBN knockdown and observed an expected change in TEAD stability. This observation and the other data presented in Figure 6 suggest that TEAD proteins are targeted for proteasomal degradation under compound D treatment.

(3) As a control, sgPARP1 or PARP1 inhibitors should be used to confirm TEAD PARylation reduction.

We thank the reviewer for the suggestion. We had attempted to assess the effect of PARP inhibitors (including veliparib and olaparib) on TEAD ubiquitination, but the data is relatively complex to interpret. Besides inhibiting PARP1/2 catalytic activities, PARP inhibitors also trap PARP on chromatin. Hence, these inhibitors could induce other cellular changes in addition to inhibit the catalytic activities of PARP1/2. Given these pitfalls, we decided not to include these inconclusive data. Even though the experiments with PARP inhibitors were inconclusive, our study supports that TEAD2 and TEAD4 are PARylated in cells using an anti-PAR antibody (Fig. 3B). Furthermore, we show that mutation of the D70 PARsylation site to alanine greatly abolished TEAD4 ubiquitination in cells, suggesting PARylation is important for TEAD4 ubiquitination. In addition, PARP1 depletion by siRNA and CRISPR guide RNA reduced TEAD2 and TEAD4 ubiquitination levels, indicating PARP1 is one of the PARPs responsible for TEAD PARylation in cells.

(4) MS data looks convincing but an FDR of 1% should be applied - this is the accepted standard in the proteomics field. Please research the data with the more stringent filter.

We thank the reviewer for the suggestion. Our IP-MS experiment comparing siNTC versus siYAP1/WWTR1 in Patu-8902 cells did not have replicates and FDR could not be derived. Therefore, we listed the raw data in Supplemental Table 3 without showing statistics. To validate the putative interactions identified by IP-MS, we performed IP-Western experiments to confirm that TEAD4 interacts with PARP1 (Figure 3A). It is important to note that in addition to our report, the interaction between PARP1 and TEADs has been observed in other publications (Calses et al., 2023; Yang et al., 2017). We have included more details of the IP-MS experiment reported in Supplemental Table 3 in the revised manuscript and cited previous work reporting TEAD-PARP1 interaction.

(5) Proofread the manuscript more thoroughly for typos and grammatical errors.

We thank the reviewer for raising this issue and have addressed it in the revision.

(6) Improve figure clarity (e.g., clearly labeling graph axes).

We apologize for the oversight. The revised manuscript contains high resolution figures.

Specific points:

Generally, the manuscript could use additional proofreading for grammar and clarity. It would not be practical to list all, but some representative examples are listed below:

Run-on: "They act through an event-driven mechanism instead of conventional occupancy-driven pharmacology, in addition, target protein degradation removes all functions of the target protein and may also lead to destabilization of entire multidomain protein complexes."

Typo: "Compound D exhibits significant inhibition of cell proliferation and downstream signaling compared to compound A, a reversible TEAD lipid pocket binder that lack the ubiquitin ligase binding moiety."

Typo: "Thus, we sought to deplete TEAD proteins by directly target them for ubiquitination and proteasomal degradation via pharmacologically inducing interactions between TEAD and other abundantly expressed and PARylation-independent E3 ligases."

Typo: "Compound A is a close in analog of Compound B as described previously (Holden et al., 2020)."

We have revised the manuscript and corrected the typos and grammatical errors listed above and beyond.

Specific comments on the figures are listed below:

Figure 2:

• Figures 2B and 2C should be separated into separate panels for clarity.

We have updated the Figures 2B and 2C as suggested.

• Figure 2C - "To further assess the function of RNF146, we depleted RNF146 by either sgRNA or siRNA." This should say either CRISPR-Cas9 KO or siRNA-mediated knockdown.

We thank the reviewer for the suggestion. We revised the text to address this issue.

• Figure 2D - y-axis is not labeled well/clearly. Additionally, there are different resolutions for the p-values on the graph (the top p-value is slightly clearer than the other two, suggesting either a different font was used or the value was pasted on top of a picture of the graph at a different resolution).

We updated the figures according to the suggestions.

• Figure S2A - "We identified three ubiquitin ligases - RNF146, TRAF3, and PH5A - as potential negative regulators for the Hippos pathway from the primary screen using the luciferase reporter." However, the siPHF5A data appears to decrease luciferase levels whereas siRNF146 and siTRAF3 increase it.

We thank the reviewer for catching this error. We removed PH5A from this list.

Figure 3:

• Figure 3A - label more clearly. Is this an endogenous TEAD4 co-IP?

We thank the reviewer for the suggestion. The experiment was an IP-mass spectrometry study in a TEAD4 amplified cell line model (PATU-8902) with pan-TEAD antibody. We have included the details to in the figure legends. Figure 3A is now Figure S3E in the revised manuscript.

• Figure 3C - why are the dark and light exposures not matching/corresponding? In the dark exposure, there are two particularly dark bands, the darkest of which is at the top of the gel. However, this darkest band disappears in the light exposure gel. Additionally, the last lane is marked as +TEAD2 and +TEAD4. Not sure if this is a typo, and meant to be only +TEAD4? Seems a bit strange to have a double TEAD lane.

We thank the reviewer for this comment and apologize for the oversight. There was a typo in the label. The light exposure image was from a replicate run instead of the same run, therefore the lanes didn’t all match up. We have removed the light exposure panel to resolve the confusion. (Figure 3B).

Figure 5:

• Figure 5B - why is shTEAD1-4/Sucrose a much higher tumor volume than shNTC/Sucrose negative control? Additionally, should the legend say "sNTC/Sucrose" as it does or "shNTC/Sucrose"?

The labels for shTEAD1-4/Sucrose and shNTC/Sucrose are correct. We do not understand why there is a slight increase in tumor volume for shTEAD1-4/Sucrose and suspect that is due to the considerable variation in the experiment. This slight change, however, doesn’t influence our observation of tumor regression in shTEAD1-4 under the Doxycycline treatment.

"sNTC/Sucrose" is a typo. We apologize for the oversight and have revised the figure.

• Figure 5E - cited in text after Figures 6 and 7.

We have updated the text accordingly.

Figure 6:

• Figure 6B - it is very interesting how this clearly shows the Hook effect for Compound D, but it's a bit harder to see for compound E that the compound degrades pan-TEAD. Would it be possible to quantify the blots to reinforce claims about protein degradation here?

We thank the reviewer for the question. There may seem to be some hook effect across the three concentrations of compound D treatment in Fig. 6B.  However, in Fig. 6C-E, we observed pretty consistent TEAD degradation levels across a variety of concentrations. In addition, these experiments have been repeated in multiple cell lines with consistent results. We respectfully argue that more detailed investigation of the hook effect is beyond the scope of our study.

Figure 7:

• Figure 7F - this heat map is extremely difficult to interpret. Are there any interesting clusters? What are the darker/lighter bands for Compound D compared to DMSO control?

We thank the reviewer for the comment and apologize for the lack of information on the figure. These are genes from a Hippo signature derived from our earlier work (Pham et al. Cancer Discovery). As a result of degrading TEAD when treating the cells with Compound D, we observed an expected downregulation of most of these genes compared to compound A.

Figure 8:

• Figure 8B - these two pie charts are also difficult to interpret. Perhaps try to present the data in a form other than encircling pie charts?

We thank the reviewer for the suggestion. However, this is a very descriptive pie chart, we used this format to save space.

• Figure 8C - what is GNE-6915? Is this Compound D?

Yes, this is compound D. The text is updated accordingly.

Reviewer #3 (Recommendations For The Authors):

Figure 3A would benefit from explicitly stating the conditions within the figure, rather than referring to the legend. This clarity is also needed for Figure 8C, indicating whether the treatment was with compound D or GNE-6915.

We thank the reviewer for the suggestion. We have added the details to the figures and made the suggested edits.

Standardize the terms "ubiquitination" and "ubiquitylation" throughout the paper for consistency.

We now use the term “ubiquitination” throughout the manuscript.

The statement "In this study, we show that the activity of TEAD transcription factors can be post-transcriptionally regulated via the ubiquitin/proteasome system" should be corrected to "post-translationally regulated."

We have update the manuscript accordingly.

There is an additional exclamation mark above Figure 5E that should be removed.

We have revised Figure 5E.

eLife Assessment

This study presents a useful model of genetic drift by incorporating variance in reproductive success, claiming to address several paradoxes in molecular evolution. However, some of the claimed "paradoxes" seem to be overstatements, as previous literature has pointed out the limitations of the standard model and proposed more advanced models to address those limitations. While the proposed new model presented in this paper yields some intriguing theoretical predictions, the analysis and simulations presented are incomplete to support the authors' strong claims, and it is unclear how much the model helps explain empirical observations.

Reviewer #1 (Public review):

The revision by Ruan et al clarifies several aspects of the original manuscript that were difficult to understand, and I think it presents some useful and interesting ideas. I understand that the authors are distinguishing their model from the standard Wright-Fisher model in that the population size is not imposed externally, but is instead a consequence of the stochastic reproduction scheme. Here, the authors chose a branching process but in principle any Markov chain can probably be used. Within this framework, the authors are particularly interested in cases where the variance in reproductive success changes through time, as explored by the DDH model, for example. They argue with some experimental results that there is a reason to believe that the variance in reproductive success does change over time.

One of the key aspects of the original manuscript that I want to engage with is the DDH model. As the authors point out, their equations 5 and 6 are assumptions, and not derived from any principles. In essence, the authors are positing that that the variance in reproductive success, given by 6, changes as a function of the current population size. There is nothing "inherent" to a negative binomial branching mechanism that results in this: in fact, the the variance in offspring number could in principle be the same for all time. As relates to models that exist in the literature, I believe that this is the key difference: unlike Cannings models, the authors allow for a changing variance in reproduction through time.

This is, of course, an interesting thing to consider, and I think that the situation the authors point out, in which drift is lower at small population sizes and larger at large population sizes, is not appreciated in the literature. However, I am not so sure that there is anything that needs to be resolved in Paradox 1. A very strong prediction of that model is that Ne and N could be inversely related, as shown by the blue line in Fig 3b. This suggests that you could see something very strange if you, for example, infer a population size history using a Wright-Fisher framework, because you would infer a population *decline* when there is in fact a population *expansion*. However, as far as I know there are very few "surprising population declines" found in empirical data. An obvious case where we know there is very rapid population growth is human populations; I don't think I've ever seen an inference of recent human demographic history from genetic data that suggests anything other than a massive population expansion. While I appreciate the authors empirical data supporting their claim of Paradox 1 (more on the empirical data later), it's not clear to me that there's a "paradox" in the literature that needs explaining so much as this is a "words of caution about interpreting inferred effective population sizes". To be clear, I think those words of caution are important, and I had never considered that you might be so fundamentally misled as to infer decline when there is growth, but calling it a "paradox" seems to suggest that this is an outstanding problem in the literature, when in fact I think the authors are raising a *new* and important problem. Perhaps an interesting thing for the authors to do to raise the salience of this point would be to perform simulations under this model and then infer effective population sizes using e.g. dadi or psmc and show that you could identify a situation in which the true history is one of growth, but the best fit would be one of decline

The authors also highlight that their approach reflects a case where the population size is determined by the population dynamics themselves, as opposed to being imposed externally as is typical in Cannings models. I agree with the authors that this aspect of population regulation is understudied. Nonetheless, several manuscripts have dealt with the case of population genetic dynamics in populations of stochastically fluctuating size. For example, Kaj and Krone (2003) show that under pretty general conditions you get something very much like a standard coalescent; for example, combining their theorem 1 with their arguments on page 36 and 37, they find that exchangeable populations with stochastic population dynamics where the variance does not change with time still converge to exactly the coalescent you would expect from Cannings models. This is strongly suggestive that the authors key result isn't about stochastic population dynamics per se, but instead related to arguing that variance in reproductive success could change through time. In fact, I believe that the result of Kaj and Krone (2003) is substantially more general than the models considered in this manuscript. That being said, I believe that the authors of this manuscript do a much better job of making the implications for evolutionary processes clear than Kaj and Krone, which is important---it's very difficult to understand from Kaj and Krone the conditions under which effective population sizes will be substantially impacted by stochastic population dynamics.

I also find the authors exposition on Paradox 3 to be somewhat strange. First of all, I'm not sure there's a paradox there at all? The authors claim that the lack of dependence of the fixation probability on Ne is a paradox, but this is ultimately not surprising---fixation of a positively selected allele depends mostly on escaping the boundary layer, which doesn't really depend on the population size (see Gillespie's book "The Causes of Molecular Evolution" for great exposition on boundary layer effects). Moreover, the authors *use a Cannings-style argument* to get gain a good approximation of how the fixation probability changes when there is non-Poisson reproduction. So it's not clear that the WFH model is really doing a lot of work here. I suppose they raise the interesting point that the particularly simple form of p(fix) = 2s is due to the assumption that variance in offspring is equal to 1.

In addition, I raised some concerns about the analysis of empirical results on reproductive variance in my original review, and I don't believe that the authors responded to it at all. I'm not super worried about that analysis, but I think that the authors should probably respond to me.

Overall, I feel like I now have a better understanding of this manuscript. However, I think it still presents its results too strongly: Paradox 1 contains important words of caution that reflect what I am confident is an under appreciated possibility, and Paradox 3 is, as far as I'm concerned, not a paradox at all. I have not addressed Paradox 2 very much because I think that another reviewer had solid and interesting comments on that front and I am leaving it to them. That being said, I do think Paradox 2 actually presents a deep problem in the literature and that the authors' argument may actually represent a path toward a solution.

This manuscript can be a useful contribution to the literature, but as it's presented at the moment, I think most of it is worded too strongly and it continues to not engage appropriately with the literature. Theoretical advances are undoubtedly important, and I think the manuscript presents some interesting things to think about but ultimately needs to be better situated and several of the claims strongly toned down.

References:<br /> Kaj, I., & Krone, S. M. (2003). The coalescent process in a population with stochastically varying size. Journal of Applied Probability, 40(1), 33-48.

Reviewer #2 (Public review):

Summary:

This theoretical paper examines genetic drift in scenarios deviating from the standard Wright-Fisher model. The authors discuss Haldane's branching process model, highlighting that the variance in reproductive success equates to genetic drift. By integrating the Wright-Fisher model with the Haldane model, the authors derive theoretical results that resolve paradoxes related to effective population size.

Strengths:

The most significant and compelling result from this paper is perhaps that the probability of fixing a new beneficial mutation is 2s/V(K). This is an intriguing and potentially generalizable discovery that could be applied to many different study systems.

The authors also made a lot of effort to connect theory with various real-world examples, such as genetic diversity in sex chromosomes and reproductive variance across different species.

Comments on revisions:

The author has addressed some of the concerns in my review, and I think the revised manuscript is more clear. I like the discussion about the caveats of the WFH model.

I hope the authors could also discuss the conditions needed for V(K)/Ne to be a reasonable approximation. It is currently unclear how the framework should be adopted in general.

The idea about estimating male-female V(K) ratios from population genetic data is interesting. Unfortunately, the results fell short. The accuracy of their estimators (derived using approximation Ne/V(K) approximation, and certain choice of theta, and then theta estimated with Watterson's estimator) should be tested with simulated results before applying to real data. The reliability of their estimator and their results from real data are unclear.

Arguments made in this paper sometimes lack precision (perhaps the authors want to emphasize intuition, but it seems more confusing than otherwise). For example: The authors stated that "This independence from N seems intuitively obvious: when an advantageous mutation increases to say, 100 copies in determining a population (depending mainly on s), its fixation would be almost certain, regardless of N.". Assuming large Ne, and with approximation, one could assume the probability of loss is e^(-2sn), but the writing about "100 copies" and "almost certain" is very imprecise, in fact, a mutation with s=0.001 segregating at 100 copies in a large Ne population is most probably lost. Whereas in a small population, it will be fixed. Yet the following sentence states "regardless of N. This may be a most direct argument against equating genetic drift, certainly no less important than 1/ N . with N, or Ne (which is supposed to be a function of N's)." I find this new paragraph misleading.

Some of the statements/wordings in this paper still seem too strong to me.

Reviewer #3 (Public review):

Summary:

Ruan and colleagues consider a branching process model (in their terminology the "Haldane model") and the most basic Wright-Fisher model. They convincingly show that offspring distributions are usually non-Poissonian (as opposed to what's assumed in the Wright-Fisher model), and can depend on short-term ecological dynamics (e.g., variance in offspring number may be smaller during exponential growth). The authors discuss branching processes and the Wright-Fisher model in the context of 3 "paradoxes" --- 1) how Ne depends on N might depend on population dynamics; 2) how Ne is different on the X chromosome, the Y chromosome, and the autosomes, and these differences do match the expectations base on simple counts of the number of chromosomes in the populations; 3) how genetic drift interacts with selection. The authors provide some theoretical explanations for the role of variance in the offspring distribution in each of these three paradoxes. They also perform some experiments to directly measure the variance in offspring number, as well as perform some analyses of published data.

Strengths:

- The theoretical results are well-described and easy to follow.<br /> - The analyses of different variances in offspring number (both experimentally and analyzing public data) are convincing that non-Poissonian offspring distributions are the norm.<br /> - The point that this variance can change as the population size (or population dynamics) change is also very interesting and important to keep in mind.<br /> - I enjoyed the Density-Dependent Haldane model. It was a nice example of the decoupling of census size and effective size.<br /> - Equation (10) is a nice result (but see below)

Weaknesses:

- I am not convinced that these types of effects cannot just be absorbed into some time-varying Ne and still be well-modeled by the Wright-Fisher process. As a concrete example, Mohle and Sagitov 2001 show that a "coalescent Ne" for the WF model should be (N-1)/Var(K). This resolves the exponentially growing bacteria "paradox" raised in the present paper --- when the bacteria are growing Var(K) ~ 0, and hence there should be very little drift. This exactly resolves the "paradox" raised by the authors. Instead, it merely underscores that Ne does not need to be equal to (or even positively correlated!) with N. I absolutely do not see this as a failure of the WF model. Whether one finds branching processes or the WF model more biologically intuitive is a matter of taste, but to say that WF models cannot explain this "paradox" is false, when a well-known paper from more than 20 years ago does just that.<br /> - Along these lines, the result that Ne in the Wright-Fisher process might not be related to N in any straightforward (or even positively correlated) way are well-known (e.g., Neher and Hallatschek 2012; Spence, Kamm, and Song 2016; Matuszewski, Hildebrandt, Achaz, and Jensen 2018; Rice, Novembre, and Desai 2018; the work of Lounès Chikhi on how Ne can be affected by population structure; etc...)<br /> - I was also missing some discussion of the relationship between the branching process and the Wright-Fisher model (or more generally Cannings' Exchangeable Models) when conditioning on the total population size. In particular, if the offspring distribution is Poisson, then conditioned on the total population size, the branching process is identical to the Wright-Fisher model.<br /> - Given that Cannings' exchangeable models decouple N and Ne, it would not surprise me if something like equation (10) could be derived under such a model. I have not seen such a derivation, and the authors' result is nice, but I do not see it as proof that WF-type models (i.e., Cannings' models) are irreparably broken.

Author response:

The following is the authors’ response to the original reviews.

eLife Assessment

This study presents a useful modification of a standard model of genetic drift by incorporating variance in offspring numbers, claiming to address several paradoxes in molecular evolution. It is unfortunate that the study fails to engage prior literature that has extensively examined the impact of variance in offspring number, implying that some of the paradoxes presented might be resolved within existing frameworks.

The prior literature the reviewers referred to are all "modified WF models". In the original submission, we lumped the standard and modified WF models together as the "generalized WF models". As the lumping causes confusions, their distinctions are now made clear.  That said, the Haldane model in our proposal is not a modification of the standard WF model because, conceptually, the two models are very different. WF is based on sampling whereas the Haldane model is based on gene transmission.

While the "modified WF models" often incorporate V(K) [variance in progeny number], the modification is still based on the WF model of population sampling. The modification is mathematically feasible but biologically untenable, as explained explicitly in the revised text. Most important, all four paradoxes are as incompatible with the modified WF models as with the standard model. Note that the Haldane model does not have the sampling step, which is absorbed into the V(K) term. In the integrated WF-Haldane model, these paradoxes are resolved (see the new sections of Discussion, quoted below).

If readers do not have time to ponder on all four paradoxes, they may simply read the first one, as follows. When the population size (N) is growing exponentially, such as in a bacteria culture, drift is nearly absent when N is small and becomes stronger as N increases, especially when approaching the carrying capacity.  Such common observations are exactly opposite of the WF model's central prediction. Any model based on sampling cannot escape the constraint of "greater drift, smaller N".

Revision - The following text is a reproduction of the last 7 paragraphs of Discussion.

“The standard WF model has been extended in several directions (overlapping generations, multiple alleles, ploidy, etc.). The modification most relevant to our studies here is the introduction of V(K) into the model, thus permitting V(K) ≠ E(K). While the modifications are mathematically valid, they are often biologically untenable. Kimura and Crow (1963) may be the first to offer a biological mechanism for V(K) ≠ E(K), effectively imposing the Haldane model on the WF model. Other models (Kimura and Crow 1963; Lynch, et al. 1995; Sjodin, et al. 2005; Der, et al. 2011; Cannings 2016) indeed model mathematically the imposition of the branching process on the population, followed by the WF sampling. The constructions of such models are biologically dubious but, more importantly, still unable to resolve the paradoxes. It would seem more logical to use the Haldane model in the first place by having two parameters, E(K) and V(K). 

Even if we permit V(K) ≠ E(K) under the WF sampling, the models would face other difficulties. For example, a field biologist needs to delineate a Mendelian population and determine its size, N or Ne. In all WF models, one cannot know what the actual population being studied is. Is it the fly population in an orchard being sampled, in the geographical region, or in the entire species range? It is unsatisfactory when a population biologist cannot identify the population being studied. The Haldane model is an individual-output model (Chen, et al. 2017), which does not require the delineation of a Mendelian population.

We shall now review the paradoxes specifically in relation to the modified WF models, starting with the multi-copy gene systems such as viruses and rRNA genes covered in the companion study (Wang, et al. 2024). These systems evolve both within and between hosts. Given the small number of virions transmitted between hosts, drift is strong in both stages as shown by the Haldane model (Ruan, Luo, et al. 2021; Ruan, Wen, et al. 2021; Hou, et al. 2023). Therefore, it does not seem possible to have a single effective population size in the WF models to account for the genetic drift in two stages. The inability to deal with multi-copy gene systems may explain the difficulties in accounting for the SARS-CoV-2 evolution (Deng, et al. 2022; Pan, Liu, et al. 2022; Ruan, Wen, et al. 2022; Hou, et al. 2023; Ruan, et al. 2023).

We now discuss the first paradox of this study, which is about the regulation of N. In the general WF models, N is imposed from outside of the model, rather than self-generating within the model. When N is increasing exponentially as in bacterial or yeast cultures, there is almost no drift when N is very low and drift becomes intense as N grows to near the carrying capacity. As far as we know, no modifications of the WF model can account for this phenomenon that is opposite of its central tenet. In the general WF models, N is really the carrying capacity, not population size. 

The second paradox of sex chromosomes is rooted in V(K) ≠ E(K). As E(K) is the same between sexes but V(K) is different, clearly V(K) = E(K) would not be feasible. The mathematical solution of defining separate Ne's for males and females (Kimura and Crow 1963; Lynch, et al. 1995; Sjodin, et al. 2005; Der, et al. 2011; Cannings 2016) unfortunately obscures the interesting biology. As shown in Wang et al. (2024; MBE), the kurtosis of the distribution of K indicates the presence of super-breeder males. While the Haldane model can incorporate the kurtosis, the modified WF models are able to absorb only up to the variance term, i.e., the second moment of the distribution. The third paradox of genetic drift is manifested in the fixation probability of an advantageous mutation, 2_s_/V(K). As explained above, the fixation probability is determined by the probability of reaching a low threshold that is independent of N itself. Hence, the key parameter of drift in the WF model, N (or Ne), is missing. This paradox supports the assertion that genetic drift is fundamentally about V(K) with N being a scaling factor. 

As the domain of evolutionary biology expands, many new systems do not fit into the WF models, resulting in the lack of a genetic drift component in their evolutionary trajectories. Multi-copy gene systems are obvious examples. Others include domestications of animals and plants that are processes of rapid evolution  (Diamond 2002; Larson and Fuller 2014; Purugganan 2019; Chen, Yang, et al. 2022; Pan, Zhang, et al. 2022; Wang, et al. 2022). Due to the very large V(K) in domestication, drift must have played a large role. Somatic cell evolution is another example with “undefinable” genetic drift (Wu, et al. 2016; Chen, et al. 2017; Chen, et al. 2019; Ruan, et al. 2020; Chen, Wu, et al. 2022). The Haldane (or WFH) model, as an "individual output" model, can handle these general cases of genetic drift.

The Haldane model and the WF model are fundamentally different approaches to random forces of evolution. While the WF models encounter many biological contradictions, they have provided approximate mathematical solutions to more realistic scenarios. In systems such as in viral evolution (Ruan, Hou, et al. 2022; Hou, et al. 2023) or somatic cell evolution (Chen, Wu, et al. 2022; Zhai, et al. 2022) whereby the WF solution is absent, further development of the WFH model will be necessary.”

In addition, while the modified model yields intriguing theoretical predictions, the simulations and empirical analyses are incomplete to support the authors' claims.

This point is addressed in the responses to reviewers' comments. Since they are quite technical, they do not fit in the overview here.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors present a theoretical treatment of what they term the "Wright-Fisher-Haldane" model, a claimed modification of the standard model of genetic drift that accounts for variability in offspring number, and argue that it resolves a number of paradoxes in molecular evolution. Ultimately, I found this manuscript quite strange.

The notion of effective population size as inversely related to the variance in offspring number is well known in the literature, and not exclusive to Haldane's branching process treatment. However, I found the authors' point about variance in offspring changing over the course of, e.g. exponential growth fairly interesting, and I'm not sure I'd seen that pointed out before.

Weaknesses:

I have several outstanding issues. First of all, the authors really do not engage with the literature regarding different notions of an effective population. Most strikingly, the authors don't talk about Cannings models at all, which are a broad class of models with non-Poisson offspring distributions that nonetheless converge to the standard Wright-Fisher diffusion under many circumstances, and to "jumpy" diffusions/coalescents otherwise (see e.g. Mohle 1998, Sagitov (2003), Der et al (2011), etc.). Moreover, there is extensive literature on effective population sizes in populations whose sizes vary with time, such as Sano et al (2004) and Sjodin et al (2005).

Of course in many cases here the discussion is under neutrality, but it seems like the authors really need to engage with this literature more.

The reviewer's summary and weakness statement reflects the general criticism summarized by the editors. The reply and revision to these criticisms have been presented in the long reply to elife assessment above.

We hence re-emphasize only the key points here.

(1) The literature that the reviewers fault us for not citing is about the modifications of the standard WF model. We now cite them as well as a few others in that vein. However, the WF-Haldane model we propose is conceptually very different from the modified WF models. This WFH model is in essence the Haldane model which may use the results of the WF models as the starting point to find the exact solutions.

(2) The check of the power of the modified WF models is whether they can resolve the paradoxes. None of them can. The arguments apply to neutral cases as well as selection effects. Hence, our central point is that the modifications of the standard WF model [e.g., by incorporating V(K)] do not help the WF model in resolving the paradoxes.  Besides, the incorporation of V(K) is mathematically feasible but biologically untenable as presented in the new sections of Discussion.

Nonetheless, I don't think the authors' modeling, simulations, or empirical data analysis are sufficient to justify their claims.

The most interesting part of the manuscript, I think, is the discussion of the Density Dependent Haldane model (DDH). However, I feel like I did not fully understand some of the derivation presented in this section, …… - this is the whole notion of exchangeability, also neglected in this manuscript). As such, I don't believe that their analysis of the empirical data supports their claim. [Since the comments above are highly technical and fairly long, they are not copied verbatim.]

We thank this reviewer for the detailed comments with respect to the potential confusion in the discussion of the Density Dependent Haldane (DDH) model.

First, the reviewer appears to ask how Eqs (5-6) are derived. We should clarify that both Eq (5) and (6) are assumptions rather than derived results. Both equations are assumptions based on population ecology. Eq (7) is then derived by substituting the assumptions in Eq (5) and (6) into Eq (3).

The definition in Equation (5) allows the growth rate of the population size to be dependent on N itself, such that growth rate E(K) (average offspring number per generation) is greater than 1 when N < Ck and less than 1 when N > Ck. The parameter z is introduced to adjust the sensitivity of E(K) to changes in population size (as shown in Fig. 3a).

Second, we appreciate the comments regarding the use of individual-based simulations and the apparent lack of interaction between individuals. In our simulations, there is indeed an interaction among individuals, which is represented by Eq (5). This equation reflects how the competition between two alleles affects the expected growth rate 𝐸(𝐾), which decreases as the population size increases. Furthermore, once 𝐸(𝐾) for the entire population is determined, the offspring numbers of the alleles are independent.

We believe that the primary purpose of our simulations was not clearly stated. This lack of clarity may be the root of the criticisms. We now note that the simulations are aimed at testing the accuracy of Equation (10).

Note that Eq. (10) is a textbook result and quite important in our study. This equation shows that the strength of genetic drift, as given by Pf (the fixation probability of an advantageous mutation), is not a function of N at all. This approximate solution has been obtained using the WF model by Kimura.  The Haldane model solution that can explain Paradox 1 is based on Equation (7) as shown below

Since the fixation probability of Equation (10) cannot be easily obtained using Eq. (7), we conducted simulations to confirm the accuracy of Eq. (10) when applied to the Haldane model.

We have revised the relevant sections of the manuscript to clarify these points and to better distinguish between assumptions and results. 

Revision - Details of the DDH model are given in the Supplementary Information. A synopsis is given here: We consider a non-overlapping haploid population with two neutral alleles. The population size at time t is Nt. We assume that expected growth rate E(K) is greater than 1 when N < Ck and less than 1 when N > Ck, as defined by Eq. (5) below:

The slope of E(K) vs. N (i.e., the sensitive of growth rate to changes in population size), as shown in Fig 3a, depends on z. To determine the variance V(K), we assume that K follows the negative binomial distribution whereby parents would suffer reproduction-arresting injury with a probability of pt at each birthing (Supplementary Information). Accordingly, V(K) can then be expressed as

By Eq. (6), the ratio of V(K)/E(K) could be constant, decrease or increase with the increase of population size. With E(K) and V(K) defined, we could obtain the effective population size by substituting Eq. (5) and Eq. (6) into Eq. (3).

Eq. (7) presents the relationship between effective population size (Ne) and the population size (N) as shown in Fig. 3. The density-dependent E(K) could regulate N with different strength (Fig. 3a). The steeper the slope in Fig. 3a, the stronger the regulation.

Simulation of genetic drift in the Haldane model and the Wright-Fisher (WF) model. In both models, interactions between individuals are implicitly included through the dependency of the average number of offspring on population size, as defined by Eq. (5). This dependency leads to the logistic population growth, reflecting the density-dependent interactions.

Thus, while I think there are some interesting ideas in this manuscript, I believe it has some fundamental issues:

first, it fails to engage thoroughly with the literature on a very important topic that has been studied extensively. Second, I do not believe their simulations are appropriate to show what they want to show. And finally, I don't think their empirical analysis shows what they want to show.

References omitted

The comments are the summary of previous ones, which have been addressed in detail in the preceding sections.

Reviewer #2 (Public Review):

Summary:

This theoretical paper examines genetic drift in scenarios deviating from the standard Wright-Fisher model. The authors discuss Haldane's branching process model, highlighting that the variance in reproductive success equates to genetic drift. By integrating the Wright-Fisher model with the Haldane model, the authors derive theoretical results that resolve paradoxes related to effective population size [Ne]

Thanks.  The issue of Ne will be addressed below where the reviewer returns to this issue. The strength of the integrated WFH model is that N (or Ne) is generated by the model itself, rather than externally imposed as in WF models.

Strengths:

The most significant and compelling result from this paper is perhaps that the probability of fixing a new beneficial mutation is 2s/V(K). This is an intriguing and potentially generalizable discovery that could be applied to many different study systems.

The authors also made a lot of effort to connect theory with various real-world examples, such as genetic diversity in sex chromosomes and reproductive variance across different species.

Thanks. 

Weaknesses:

One way to define effective population size is by the inverse of the coalescent rate. This is where the geometric mean of Ne comes from. If Ne is defined this way, many of the paradoxes mentioned seem to resolve naturally. If we take this approach, one could easily show that a large N population can still have a low coalescent rate depending on the reproduction model. However, the authors did not discuss Ne in light of the coalescent theory. This is surprising given that Eldon and Wakeley's 2006 paper is cited in the introduction, and the multiple mergers coalescent was introduced to explain the discrepancy between census size and effective population size, superspreaders, and reproduction variance - that said, there is no explicit discussion or introduction of the multiple mergers coalescent.

The Haldane model treats N’s very differently from the WF models.  In the WF models, N’s are imposed externally (say, constant N, exponentially growing N, temporally fluctuating N’s and so on; all provided from outside of the model). Ne and coalescence are all derived from these given N’s.  In order to account for the first paradox (see the next paragraph), N needs to be regulated but the WF models cannot regulate N’s. The density-dependent Haldane model that Reviewer 1 inquired above is a model that regulates N internally. It can thus account for the paradox.

Paradox 1 -  When the population size (N) is growing exponentially, such as in a bacteria culture, drift is nearly absent when N is small and is much stronger as N increases, especially when approaching the carrying capacity.  Such a pattern is a common observation and is exactly opposite of the WF model's central prediction. In short, a model that does not regulate N cannot explain the paradox

Ne is a fix of the WF model in order to account for the missing components of genetic drift. The paradoxes presented in this one and the companion study show that the fix is rather inadequate.  In contrast, by the WFH model, N is regulated within the model itself as E(K) and V(K) are both functions of N.

The Wright-Fisher model is often treated as a special case of the Cannings 1974 model, which incorporates the variance in reproductive success. This model should be discussed. It is unclear to me whether the results here have to be explained by the newly introduced WFH model, or could have been explained by the existing Cannings model. The abstract makes it difficult to discern the main focus of the paper. It spends most of the space introducing "paradoxes".

We appreciate greatly the illuminating advice.  Nevertheless, we should explain, or should have explained, more clearly that these four paradoxes presented are central to this pair of eLife papers. The WF and Haldane models are very different conceptual ideas altogether. The choice should not be based on mathematical grounds but on how they help us understand biological evolution. We are using four paradoxes to highlight the differences.  We have said in the papers that the origin and evolution of COVID-19 caused a lot of confusions partly because the WF models cannot handle multi-copy gene systems, including viruses that evolve both within- and between- hosts.

The standard Wright-Fisher model makes several assumptions, including hermaphroditism, non-overlapping generations, random mating, and no selection. It will be more helpful to clarify which assumptions are being violated in each tested scenario, as V(K) is often not the only assumption being violated. For example, the logistic growth model assumes no cell death at the exponential growth phase, so it also violates the assumption about non-overlapping generations.

We appreciate the question which has two aspects.  First, why do we think the WF models are insufficient? After all, for each assumption of the WF model (as given in the reviewer’s examples), there is often a solution by modifying Ne which relaxes the assumption. In this sense, there is only one grand assumption made by the WF models. That is, however complex the biology is, it is possible to find Ne that can make the WF model work. Our argument is that Ne is a cumbersome fix of the WF model and it does not work in many situations. That is how we replied about the importance of the paradoxes above.  We shall again use the first paradox as an example whereby drift is stronger as N becomes larger, the fix has to make Ne negatively correlated with N. In reality, it does not appear possible to resolve this paradox. Another paradox is the evolution of multi-copy gene systems. In short, it seems clear that Ne is not a useful or usable fix.

The second aspect is that “why, among the many modifications the WF models make, do we only emphasize the inclusion of V(K)?” This is the essence of the two papers of ours.  Although V(K) is a modification of the WF models, it does not enable the WF models to resolve the paradoxes. In contrast, the Haldane model has incorporate E(K) and V(K) in the model. In presenting paradox 3, it was stated that

This equation shows that the strength of genetic drift, as given by Pf (the fixation probability of an advantageous mutation), is not a function of N at all. It supports the view that the essence of genetic drift is V(K) with N as a scaling factor. Note that, if V(K) = 0, there is no genetic drift regardless of N. As V(K) is not an add-on to the Haldane model (unlike in WF models), the Haldane model can resolve the paradoxes.

The theory and data regarding sex chromosomes do not align. The fact that \hat{alpha'} can be negative does not make sense. The authors claim that a negative \hat{alpha'} is equivalent to infinity, but why is that? It is also unclear how theta is defined. It seems to me that one should take the first principle approach e.g., define theta as pairwise genetic diversity, and start with deriving the expected pair-wise coalescence time under the MMC model, rather than starting with assuming theta = 4Neu. Overall, the theory in this section is not well supported by the data, and the explanation is insufficient.

a' can be negative for the same reason that a (the male/female ratio in mutation rate) can be negative (Miyata, et al. 1987; Li, et al. 2002; Makova and Li 2002). Clearly, this has not been a problem in the large literature on a becoming negative.  In fact, in many reports, a is negative, which is read as a approaching infinity.  Imagine that our equation is a'^2 = 0.25, then a' can be 0.5 or -0.5, although the latter solution is not biologically meaningful.

As for theta, the reviewer asked why we do not use the pairwise genetic diversity (or theta[pi]) as the first-principle approach to estimating theta. While theta(pi) is the first estimator of theta used, the general principle is that every bin of the frequency spectrum can be used for estimating theta since the expected value is theta/i where i is the occurrence of the mutation in the sample.  (If the sample size is 100, then i is between 1 and 99.)  Hence, the issue is which part of the spectrum has the best statistical properties for the questions at hand.  The pairwise measure is theta(pi) [which the reviewer recommends]. While theta(pi) and theta(w) are most commonly used, there are in fact numerous ways to estimate theta.  ((Fu 2022) presents an excellent review.) For our purpose, we need a theta estimate least affected by selection and we choose the lowest frequency bin of the spectrum, which is theta(1) based on the singletons. Theta(1), least affected by selection, is the basis of the Fu and Li test. 

Reviewer #3 (Public Review):

Summary:

Ruan and colleagues consider a branching process model (in their terminology the "Haldane model") and the most basic Wright-Fisher model. They convincingly show that offspring distributions are usually non-Poissonian (as opposed to what's assumed in the Wright-Fisher model), and can depend on short-term ecological dynamics (e.g., variance in offspring number may be smaller during exponential growth). The authors discuss branching processes and the Wright-Fisher model in the context of 3 "paradoxes": (1) how Ne depends on N might depend on population dynamics; (2) how Ne is different on the X chromosome, the Y chromosome, and the autosomes, and these differences do match the expectations base on simple counts of the number of chromosomes in the populations; (3) how genetic drift interacts with selection. The authors provide some theoretical explanations for the role of variance in the offspring distribution in each of these three paradoxes. They also perform some experiments to directly measure the variance in offspring number, as well as perform some analyses of published data.

Strengths:

(1) The theoretical results are well-described and easy to follow.

(2) The analyses of different variances in offspring number (both experimentally and analyzing public data) are convincing that non-Poissonian offspring distributions are the norm.

(3) The point that this variance can change as the population size (or population dynamics) change is also very interesting and important to keep in mind.

(4) I enjoyed the Density-Dependent Haldane model. It was a nice example of the decoupling of census size and effective size.

Thanks.

Weaknesses:

(1) I am not convinced that these types of effects cannot just be absorbed into some time-varying Ne and still be well-modeled by the Wright-Fisher process.

Please allow us to refer to, again, two of the four paradoxes.  We believe that that no modification of the WF model can resolve the paradoxes.

(1) When the population size (N) is growing exponentially, such as in a bacteria culture, drift is nearly absent when N is small and is much stronger as N increases, especially when approaching the carrying capacity.  Such common observations are exactly opposite of the WF model's key prediction. It is not possible for a model that does not regulate N to explain the paradox.

(2) There is no way the WF models can formulate Ne for, say viruses or ribosomal RNA genes that have two levels of populations – the within-host populations as well as the host population itself.

The fact that there are numerous Ne's suggests that Ne is a collection of cumbersome fixes of the WF model. By the WF-Haldane model, all factors are absorbed into V(K) resulting in a simpler model in the end. V(K) is often a measurable quantity. Note that, even if V(K) is incorporated into the WF model, the paradoxes remain unresolvable.

(2) Along these lines, there is well-established literature showing that a broad class of processes (a large subset of Cannings' Exchangeable Models) converge to the Wright-Fisher diffusion, even those with non-Poissonian offspring distributions (e.g., Mohle and Sagitov 2001). E.g., equation (4) in Mohle and Sagitov 2001 shows that in such cases the "coalescent Ne" should be (N-1) / Var(K), essentially matching equation (3) in the present paper.

The criticism of lack of engagement with well-established literature has been responded extensively above.  Briefly, the literature is about modifications of the WF model which share the same feature of population sampling. With that feature, the paradoxes are unresolvable.  For example, however Ne is defined, the fixation probability of an advantageous mutation does not depend on N or Ne. This is the third paradox of the WF models.

(3) Beyond this, I would imagine that branching processes with heavy-tailed offspring distributions could result in deviations that are not well captured by the authors' WFH model. In this case, the processes are known to converge (backward-in-time) to Lambda or Xi coalescents (e.g., Eldon and Wakely 2006 or again in Mohle and Sagitov 2001 and subsequent papers), which have well-defined forward-in-time processes.

We admire the learned understanding of the literature expressed by the review, which raise two points.  First, our model may not be able to handle the heavy-tailed progeny distribution (i.e., the kurtosis of the distribution of k). Second, the Xi coalescence models (cited above) can do that.  Below are our clarifications.

First, the WFH model is based on the general distribution of K, which includes flexible and realistic representations of offspring number distributions. In fact, we have used various forms of K distribution in our publications on the evolution of SARS-CoV-2 (see the Ruan et al publications in the bibliography). Power-law distribution is particularly useful as the K-distribution in viral transmission is highly kurtotic. This is reflected in the super-spreader hypothesis. In short, the branching process on which the WFH model is based in is mainly about the distribution of K. Nevertheless, the variance V(K) can often yield good approximations when the kurtosis is modest.

Second, we would like to comment on the models of Eldon and Wakely 2006. or Mohle and Sagitov 2001 and subsequent papers. These papers are based on the Moran model by considering a highly skewed distribution of offspring numbers. Fundamentally, the Moran models generally behave like WF models (standard or modified) and hence have the same problems with the paradoxes that are central to our studies. In fact, the reservations about introducing V(K) into the WF models apply as well to the Moran models.  The introduction of V(K) is mathematically valid but biologically untenable. Essentially, the WF models incorporate the Haldane model as a first step in the generation transition. The introduction of V(K) into the Moran model is even less biologically sensible. Furthermore, the model allows K to take only three discrete values: 0, 2, and Nψ (see Eq. (7) in Eldon and Wakely). Their model also assumes a constant population size, which contrasts with our model's flexibility in handling varying population sizes and more complex distributions for K.

In short, the modifications of the WF (and Moran) models are unnecessarily complicated, biologically untenable but still fail to account for the paradoxes. The WFH model can rectify these problems. 

(4) These results that Ne in the Wright-Fisher process might not be related to N in any straightforward (or even one-to-one) way are well-known (e.g., Neher and Hallatschek 2012; Spence, Kamm, and Song 2016; Matuszewski, Hildebrandt, Achaz, and Jensen 2018; Rice, Novembre, and Desai 2018; the work of Lounès Chikhi on how Ne can be affected by population structure; etc...)

The reviewer is correct in pointing out the inexact correlation between N and Ne. Nevertheless, it should still be true that the WF models predict qualitatively weaker drift as N increases. The first paradox is as stated:

When the population size (N) is growing exponentially, such as in a bacteria culture, drift is nearly absent when N is small and is much stronger as N increases, especially when approaching the carrying capacity.  Such common observations are exactly opposite of the WF model's key prediction.

(5) I was also missing some discussion of the relationship between the branching process and the Wright-Fisher model (or more generally Cannings' Exchangeable Models) when conditioning on the total population size. In particular, if the offspring distribution is Poisson, then conditioned on the total population size, the branching process is identical to the Wright-Fisher model.

We thank the reviewer for this important comment. The main difference is that N is imposed from outside the WF models but can be generated from within the Haldane model (see the density-dependent Haldane model). In nature, N of the next generation is the sum of K’s among members of the population. It is how the Haldane model determines N(t+1) from N(t). In the WF models, N is imposed from outside the model and, hence the given N determines the distribution of K.  For this reason, N regulation is not possible in the WF models, thus resulting in the paradoxes.

(6) In the discussion, it is claimed that the last glacial maximum could have caused the bottleneck observed in human populations currently residing outside of Africa. Compelling evidence has been amassed that this bottleneck is due to serial founder events associated with the out-of-Africa migration (see e.g., Henn, Cavalli-Sforza, and Feldman 2012 for an older review - subsequent work has only strengthened this view). For me, a more compelling example of changes in carrying capacity would be the advent of agriculture ~11kya and other more recent technological advances.

We thank the reviewer and have used this more convincing case as suggested by the reviewer.

Recommendations for the authors:

General replies - We thank the editors and reviewers again.  The points below are re-iterations of the comments received above and have since been replied in detail. Specific instructions about wording and notations have also been rectified. Again, we are grateful for the inputs from which we learned a great deal.

Reviewing Editor Comments:

The reviewers recognize the value of this model and some of the findings, particularly results from the density-dependent Haldane model. However, they expressed considerable concerns with the model and overall framing of this manuscript.

First, all reviewers pointed out that the manuscript does not sufficiently engage with the extensive literature on various models of effective population size and genetic drift, notably lacking discussion on Cannings models and related works.

We have addressed this issue in the beginning of Introduction and Discussion, pointing to the long section in the new second half of Discussion. The essence is that the literature is all about the modified WF models.  The WF-Haldane model is conceptually and operationally distinct from the WF models, either standard or modified ones,

Second, there is a disproportionate discussion on the paradoxes, yet some of the paradoxes might already be resolved within current theoretical frameworks. All three reviewers found the modeling and simulation of the yeast growth experiment hard to follow or lacking justification for certain choices. The analysis approach of sex chromosomes is also questioned.

This criticism is addressed together with the next one as they make the same point.

The reviewers recommend a more thorough review of relevant prior literature to better contextualize their findings. The authors need to clarify and/or modify their derivations and simulations of the yeast growth experiment to address the identified caveats and ensure robustness. Additionally, the empirical analysis of the sex chromosome should be revisited, considering alternative scenarios rather than relying solely on the MSE, which only provides a superficial solution. Furthermore, the manuscript's overall framing should be adjusted to emphasize the conclusions drawn from the WFH model, rather than focusing on the "unresolved paradoxes", as some of these may be more readily explained by existing frameworks. Please see the reviewers' overall assessment and specific comments.

Many thanks.  We have carefully reframed and presented the WF-Haldane model to make it clear and logically consistent. Whether a new model (i.e., the WF-Haldane model) deserves to be introduced depends on whether it makes any contribution for understanding nature. That is why we emphasize the four paradoxes. 

A most important disagreement between the reviewers and the authors is about the nature of the paradoxes. While the reviewers suggest that they "may" be resolvable by the conventional WF model (standard or modified), they did not offer the possible resolutions.  To use the analogy in our provisional response: the WF vs. Haldane models are compared to gas cars vs electric vehicles.  We can say confidently that the internal combustion engine cannot resolve the conflicting demands of transportation and zero emission. Its design has limited its capability. 

Reviewer #2 (Recommendations For The Authors):

Many thanks.  We have incorporated all these suggestions.  When the incorporation is not straightforward, we have carefully revised the text to minimize mis-communications.

In the introduction -- "Genetic drift is simply V(K)" -- this is a very strong statement. You can say it is inversely proportional to V(K), but drift is often defined based on changes in allele frequency.

We change the word “simply” to “essentially”. This wording is supported by the fixation probability of advantageous mutations, 2s/(V(k). We have shown in the text that N does not matter here because the fixation is nearly deterministic when the copy number reaches, say, 100, regardless of whether N is 10^4 or 10^8,

Page 3 line 86. "sexes is a sufficient explanation."--> "sex could be a sufficient explanation"

The strongest line of new results is about 2s/V(K). Perhaps, the paper could put more emphasis on this part and demonstrate the generality of this result with a different example.

The math notations in the supplement are not intuitive. e.g., using i_k and j_k as probabilities. I also recommend using E[X] and V[X]for expectation and variance rather than \italic{E(X)} to improve the readability of many equations.

Thank you for your careful reading. Regarding the use of i_k and j_k  as probabilities, we initially considered using 𝑝 or 𝑞 to represent probabilities. However, since 𝑝 and 𝑞 are already used in the main text, we opted for 𝑖 and 𝑗 to avoid potential confusion potential confusion. As for your recommendation to use

E[X] and V[X] for expectation and variance, we would like to clarify that we follow the standard practice of italicizing these symbols to represent variables.

Eq A6, A7, While I manage to follow, P_{10}(t) and P_{10} are not defined anywhere in the text.<br /> Supplement page 7, the term "probability of fixation" is confusing in a branching model.

Thank you for your observation. We have carefully revised the supplement to provide clarity on these points.<br /> Revision - In population genetics, the fixation of M allele means that the population consist entirely of the M allele, with no W alleles remaining. We define the fixation probability of M allele by generation t as follows:

Given that M and W allele reproduce independently, this can be factored as:

As t approaches infinity, the ultimate fixation probability of M allele can be derived as follows:

E.q. A 28. It is unclear eq. A.1 could be used here directly. Some justification would be nice.

We appreciate your careful review, and we will ensure this connection between the two equations is made clearer in the supplement. 

Revision - Note we would like to clarify that Eq. (A1) and Eq. (A28) are essentially the same, with the only difference being the subscript 𝑡, which indicates the time dependence in the dynamic process.

Supplement page 17. "the biological meaning of negative..". There is no clear justification for this claim. As a reader, I don't have any intuition as to why that is the case.

Thank you for raising this concern. We have addressed this issue earlier.

eLife Assessment

In this important study, the authors employed three types of theoretical/computational models (coarse-grained molecular dynamics, analytical theory and field-theoretical simulations) to analyze the impact of salt on protein liquid-liquid phase separation. These different models reinforce each other and together provide convincing evidence to explain distinct salt effects on ATP mediated phase separation of different variants of caprin1. The insights and general approach are broadly applicable to the analysis of protein phase separation. Still, modeling at the coarse-grained level misses key effects that have been revealed by all-atom simulations, including salt-backbone coordination and strengthening of pi-type interactions by salt.

Reviewer #1 (Public review):

Summary:

The authors used multiple approaches to study salt effects in liquid-liquid phase separation (LLPS). Results on both wild-type Caprin1 and mutants and on different types of salts contribute to a comprehensive understanding.

Strengths:

The main strength of this work is the thoroughness of investigation. This aspect is highlighted by the multiple approaches used in the study, and reinforced by the multiple protein variants and different salts studied.

Weaknesses:

(1) The multiple computational approaches are a strength, but they're cruder than explicit-solvent all-atom molecular dynamics (MD) simulations and may miss subtle effects of salts. In particular, all-atom MD simulations demonstrate that high salt strengthens pi-types of interactions (ref. 42 and MacAinsh et al, https://www.biorxiv.org/content/10.1101/2024.05.26.596000v3).<br /> (2) The paper can be improved by distilling the various results into a simple set of conclusions. By example, based on salt effects revealed by all-atom MD simulations, MacAinsh et al. presented a sequence-based predictor for classes of salt dependence. Wild-type Caprin1 fits right into the "high net charge" class, with a high net charge and a high aromatic content, showing no LLPS at 0 NaCl and an increasing tendency of LLPS with increasing NaCl. In contrast, pY-Caprin1 belongs to the "screening" class, with a high level of charged residues and showing a decreasing tendency of LLLPS.<br /> (3) Mechanistic interpretations can be further simplified or clarified. (i) Reentrant salt effects (e.g., Fig. 4a) are reported but no simple explanation seems to have been provided. Fig. 4a,b look very similar to what has been reported as strong-attraction promotor and weak-attraction suppressor, respectively (ref. 50; see also PMC5928213 Fig. 2d,b). According to the latter two studies, the "reentrant" behavior of a strong-attraction promotor, CL- in the present case, is due to Cl-mediated attraction at low to medium [NaCl] and repulsion between Cl- ions at high salt. Do the authors agree with this explanation? If not, could they provide another simple physical explanation? (ii) The authors attributed the promotional effect of Cl- to counterion-bridged interchain contacts, based on a single instance. There is another simple explanation, i.e., neutralization of the net charge on Caprin1. The authors should analyze their simulation results to distinguish net charge neutralization and interchain bridging; see MacAinsh et al.<br /> (4) The authors presented ATP-Mg both as a single ion and as two separate ions; there is no explanation of which of the two versions reflects reality. When presenting ATP-Mg as a single ion, it's as though it forms a salt with Na+. I assume NaCl, ATP, and MgCl2 were used in the experiment. Why is Cl- not considered? Related to this point, it looks ATP is just another salt ion studied and much of the Results section is on NaCl, so the emphasis of ATP ("Diverse Roles of ATP" in the title is somewhat misleading.

Comments on revisions:

This revision addressed all my previous comments.

Reviewer #2 (Public review):

Summary:

In this paper, Lin and colleagues aim to understand the role of different salts on the phase behavior of a model protein of significant biological interest, Caprin1, and its phosphorylated variant, pY-Caprin1. To achieve this, the authors employed a variety of methods to complement experimental studies and obtain a molecular-level understanding of ion partitioning inside biomolecular condensates. A simple theory based on rG-RPA is shown to capture the different salt dependencies of Caprin1 and pY-Caprin1 phase separation, demonstrating excellent agreement with experimental results. The application of this theory to multivalent ions reveals many interesting features with the help of multicomponent phase diagrams. Additionally, the use of CG model-based MD simulations and FTS provides further clarity on how counterions can stabilize condensed phases.

Strengths:

The greatest strength of this study lies in the integration of various methods to obtain complementary information on thermodynamic phase diagrams and the molecular details of the phase separation process. The authors have also extended their previously proposed theoretical approaches, which should be of significant interest to other researchers. Some of the findings reported in this paper, such as bridging interactions, are likely to inspire new studies using higher-resolution atomistic MD simulations.

Reviewer #3 (Public review):

Authors first use rG-RPA to reproduce two observed trends. Caprin1 does not phase separate at very low salt but then undergoes LLPS with added salt while further addition of salt reduces its propensity to LLPS. On the other hand pY-Caprin1 exhibits a monotonic trend where the propensity to phase separate decreases with the addition of salt. This distinction is captured by a two component model and also when salt ions are explicitly modeled as a separate species with a ternary phase diagram. The predicted ternary diagrams (when co and counter ions are explicitly accounted for) also predict the tendency of ions to co-condense or exclude proteins in the dense phase. Predicted trends are generally in line with the measurement for Cparin1. Next, the authors seek to explain the observed difference in phase separation when Arginines are replaced by Lysines creating different variants. In the current rG-RPA type models both Arginine (R) and Lysine (K) are treated equally since non-electrostatic effects are only modeled in a mean-field manner that can be fitted but not predicted. For this reason, coarse grain MD simulation is suitable. Moreover, MD simulation affords structural features of the condensates. They used a force field that is capable of discriminating R and K. The MD predicted degrees of LLPS of these variants again is consistent with the measurement. One additional insight emerges from MD simulations that a negative ion can form a bridge between two positively charged residues on the chain. These insights are not possible to derive from rG-RPA. Both rG-RPA and MD simulation become cumbersome when considering multiple types of ions such as Na, Cl, [ATP] and [ATP-Mg] all present at the same time. FTS is well suited to handle this complexity. FTS also provides insights into the co-localization of ions and proteins that is consistent with NMR. By using different combinations of ions they confirm the robustness of the prediction that Caprin1 shows salt-dependent reentrant behavior, adding further support that the differential behavior of Caprin1, and pY-Caprin1 is likely to be mediated by charge-charge interactions.

Comments on revisions:

The authors addressed my comments and it is ready for publication.

Author response:

The following is the authors’ response to the current reviews.

Reviewer #1 (Public Review):

Comments on revisions:

This revision addressed all my previous comments.

Reviewer #3 (Public Review):

Comments on revisions:

The authors addressed my comments and it is ready for publication.

We are grateful for the reviewers’ effort and are encouraged by their generally positive assessment of our manuscript.

Reviewer #1 (Recommendations For The Authors):

This revision addressed all my previous comments. The only new issue concerns the authors’ response to the following comment of reviewer 3:

(2) Authors note ”monovalent positive salt ions such as Na+ can be attracted, somewhat counterintuitively, into biomolecular condensates scaffolded by positively-charged polyelectrolytic IDRs in the presence of divalent counterions”. This may be due to the fact that the divalent negative counterions present in the dense phase (as seen in the ternary phase diagrams) also recruit a small amount of Na+.

Author reply: The reviewer’s comment is valid, as a physical explanation for this prediction is called for. Accordingly, the following sentence is added to p. 10, lines 27-29: ...

Here are my comments on this issue. Most IDPs with a net positive charge still have negatively charged residues, which in theory can bind cations. In fact, Caprin1 has 3 negatively charged residues (same as A1-LCD). All-atom simulations of MacAinsh et al (ref 72) have shown that these negatively charged residues bind Na+; I assume this effect can be captured by the coarsegrained models in the present study. Moreover, all-atom simulations showed that Na+ has a strong tendency to be coordinated by backbone carbonyls, which of course are present on all residues. Suggestions:

(a) The authors may want to analyze the binding partners of Na+. Are they predominantly the3 negatively charged residues, or divalent counterions, or both?

(b) The authors may want to discuss the potential underestimation of Na+ inside Caprin1 condensates due to the lack of explicit backbone carbonyls that can coordinate Na+ in their models. A similar problem applies to backbone amides that can coordinate anions, but to a lesser extent (see Fig. 3A of ref 72).

The reviewer’s comments are well taken. Regarding the statement in the revised manuscript “This phenomenon arises because the positively charge monovalent salt ions are attracted to the negatively charged divalent counterions in the protein-condensed phase.”, it should be first noted that the statement was inferred from the model observation that Na+ is depleted in condensed Caprin1 (Fig. 2a) when the counterion is monovalent (an observation that was stated almost immediately preceding the quoted statement). To make this logical connection clearer as well as to address the reviewer’s point about the presence of negatively charged residues in Caprin1, we have modified this statement in the Version of Record (VOR) as follows:

“This phenomenon most likely arises from the attraction of the positively charge monovalent salt ions to the negatively charged divalent counterions in the proteincondensed phase because although the three negatively charged D residues in Caprin1 can attract Na+, it is notable that Na+ is depleted in condensed Caprin1 when the counterion is monovalent (Fig. 2a).”

The reviewer’s suggestion (a) of collecting statistics of Na+ interactions in the Caprin1 condensate is valuable and should be attempted in future studies since it is beyond the scope of the present work. Thus far, our coarse-grained molecular dynamics has considered only monovalent Cl− counterions. We do not have simulation data for divalent counterions.

Following the reviewer’s suggestion (b), we have now added the following sentence in Discussion under the subheading “Effects of salt on biomolecular LLPS”:

“In this regard, it should be noted that positively and negatively charged salt ions can also coordinate with backbone carbonyls and amides, respectively, in addition to coordinating with charged amino acid sidechains (MacAinsh et al., eLife 2024). The impact of such effects, which are not considered in the present coarse-grained models, should be ascertained by further investigations using atomic simulations (MacAinsh et al., eLife 2024; Rauscher & Pom`es, eLife 2017; Zheng et al., J Phys Chem B 2020).”

Here we have added a reference to Rauscher & Pom`es, eLife 2017 to more accurately reflect progress made in atomic simulations of biomolecular condensates.

More generally, regarding the reviewer’s comments on the merits of coarse-grained versus atomic approaches, we re-emphasize, as stated in our paper, that these approaches are complementary. Atomic approaches undoubtedly afford structurally and energetically high-resolution information. However, as it stands, simulations of the assembly-disassembly process of biomolecular condensate are nonideal because of difficulties in achieving equilibration even for a small model system with < 10 protein chains (MacAinsh et al., eLife 2024) although well-equilibrated simulations are possible for a reasonably-sized system with ∼ 30 chains when the main focus is on the condensed phase (Rauscher & Pom`es, eLife 2017). In this context, coarse-grained models are valuable for assessing the energetic role of salt ions in the thermodynamic stability of biomolecular condensates of physically reasonable sizes under equilibrium conditions.

In addition to the above minor additions, we have also added citations in the VOR to two highly relevant recent papers: Posey et al., J Am Chem Soc 2024 for salt-dependent biomolecular condensation (mentioned in Dicussion under subheadings “Tielines in protein-salt phase diagrams” and “Counterion valency” together with added references to Hribar et al., J Am Chem Soc 2002 and Nostro & Ninham, Chem Rev 2012 for the Hofmeister phenomena discussed by Posey et al.) and Zhu et al., J Mol Cell Biol 2024 for ATP-modulated reentrant behavior (mentioned in Introduction). We have also added back a reference to our previous work Lin et al., J Mol Liq 2017 to provide more background information for our formulation.

Reviewer #2 (Recommendations For The Authors):

The authors have done a great job addressing previous comments.

We thank this reviewer for his/her effort and are encouraged by the positive assessment of our revised manuscript.

---

The following is the authors’ response to the original reviews.

Reviewer #1 (Public Review):

Summary:

The authors used multiple approaches to study salt effects in liquid-liquid phase separation (LLPS). Results on both wild-type Caprin1 and mutants and on different types of salts contribute to a comprehensive understanding.

Strengths:

The main strength of this work is the thoroughness of investigation. This aspect is highlighted by the multiple approaches used in the study, and reinforced by the multiple protein variants and different salts studied.

We are encouraged by this positive overall assessment.

Weaknesses: (1) The multiple computational approaches are a strength, but they’re cruder than explicit-solvent all-atom molecular dynamics (MD) simulations and may miss subtle effects of salts. In particular, all-atom MD simulations demonstrate that high salt strengthens pi-types of interactions (ref. 42 and MacAinsh et al, https://www.biorxiv.org/content/10.1101/2024.05.26.596000v3).

The relative strengths and limitations of coarse-grained vs all-atom simulation are now more prominently discussed beginning at the bottom of p. 5 through the first 8 lines of p. 6 of the revised manuscript (page numbers throughout this letter refer to those in the submitted pdf file of the revised manuscript), with MacAinsh et al. included in this added discussion (cited as ref. 72 in the revised manuscript). The fact that coarse-grained simulation may not provide insights into more subtle structural and energetic effects afforded by all-atom simulations with regard to π-related interaction is now further emphasized on p. 11 (lines 23–30), with reference to MacAinsh et al. as well as original ref. 42 (Krainer et al., now ref. 50 in the revised manuscript).

(2) The paper can be improved by distilling the various results into a simple set of conclusions. By example, based on salt effects revealed by all-atom MD simulations, MacAinsh et al. presented a sequence-based predictor for classes of salt dependence. Wild-type Caprin1 fits right into the “high net charg”e class, with a high net charge and a high aromatic content, showing no LLPS at 0 NaCl and an increasing tendency of LLPS with increasing NaCl. In contrast, pY-Caprin1 belongs to the “screening” class, with a high level of charged residues and showing a decreasing tendency of LLPS.

This is a helpful suggestion. We have now added a subsection with heading “Overview of key observations from complementary approaches” at the beginning of the “Results” section on p. 6 (lines 18–37) and the first line of p. 7. In the same vein, a few concise sentences to summarize our key results are added to the first paragraph of “Discussion” (p. 18, lines 23– 26). In particular, the relationship of Caprin1 and pY-Caprin1 with the recent classification by MacAinsh et al. (ref. 72) in terms of “high net charge” and “screening” classes is now also stated, as suggested by this reviewer, on p. 18 under “Discussion” (lines 26–30).

(3) Mechanistic interpretations can be further simplified or clarified. (i) Reentrant salt effects (e.g., Fig. 4a) are reported but no simple explanation seems to have been provided. Fig. 4a,b look very similar to what has been reported as strong-attraction promotor and weak-attraction suppressor, respectively (ref. 50; see also PMC5928213 Fig. 2d,b). According to the latter two studies, the “reentrant” behavior of a strong-attraction promotor, CL- in the present case, is due to Cl-mediated attraction at low to medium [NaCl] and repulsion between Cl- ions at high salt. Do the authors agree with this explanation? If not, could they provide another simple physical explanation? (ii) The authors attributed the promotional effect of Cl- to counterionbridged interchain contacts, based on a single instance. There is another simple explanation, i.e., neutralization of the net charge on Caprin1. The authors should analyze their simulation results to distinguish net charge neutralization and interchain bridging; see MacAinsh et al.

The relationship of Cl− in bridging and neutralizing configurations, respectively, with the classification of “strong-attraction promoter” and “weak-attraction suppressor” by Zhou and coworkers is now stated on p. 13 (lines 29–31), with reference to original ref. 50 by Ghosh, Mazarakos & Zhou (now ref. 59 in the revised manuscript) as well as the earlier patchy particle model study PMC5928213 by Nguemaha & Zhou, now cited as ref. 58 in the revised manuscript. After receiving this referee report, we have conducted an extensive survey of our coarse-grained MD data to provide a quantitative description of the prevalence of counterion (Cl−) bridging interactions linking positively charged arginines (Arg+s) on different Caprin1 chains in the condensed phase (using the [Na+] = 0 case as an example). The newly compiled data is reported under a new subsection heading “Explicit-ion MD offers insights into counterion-mediated interchain bridging interactions among condensed Caprin1 molecules” on p. 12 (last five lines)–p. 14 (first 10 lines) [∼ 1_._5 additional page] as well as a new Fig. 6 to depict the statistics of various Arg+–Cl−–Arg+ configurations, with the conclusion that a vast majority (at least 87%) of Cl− counterions in the Caprin1-condensed phase engage in favorable condensation-driving interchain bridging interactions.

(4) The authors presented ATP-Mg both as a single ion and as two separate ions; there is no explanation of which of the two versions reflects reality. When presenting ATP-Mg as a single ion, it’s as though it forms a salt with Na+. I assume NaCl, ATP, and MgCl2 were used in the experiment. Why is Cl- not considered? Related to this point, it looks like ATP is just another salt ion studied and much of the Results section is on NaCl, so the emphasis of ATP (“Diverse Roles of ATP” in the title is somewhat misleading.

We model ATP and ATP-Mg both as single-bead ions (in rG-RPA) and also as structurally more realistic short multiple-bead polymers (in field-theoretic simulation, FTS). We have now added discussions to clarify our modeling rationale in using and comparing different models for ATP and ATP-Mg, as follows:

p. 8 (lines 19–36):

“The complementary nature of our multiple methodologies allows us to focus sharply on the electrostatic aspects of hydrolysis-independent role of ATP in biomolecular condensation by comparing ATP’s effects with those of simple salt. Here, Caprin1 and pY-Caprin1 are modeled minimally as heteropolymers of charged and neutral beads in rG-RPA and FTS. ATP and ATP-Mg are modeled as simple salts (singlebead ions) in rG-RPA whereas they are modeled with more structural complexity as short charged polymers (multiple-bead chains) in FTS, though the latter models are still highly coarse-grained. Despite this modeling difference, rG-RPA and FTS both rationalize experimentally observed ATP- and NaCl-modulated reentrant LLPS of Caprin1 and a lack of a similar reentrance for pY-Caprin1 as well as a prominent colocalization of ATP with the Caprin1 condensate. Consistently, the same contrasting trends in the effect of NaCl on Caprin1 and pY-Caprin1 are also seen in our coarse-grained MD simulations, though polymer field theories tend to overestimate LLPS propensity [99]. The robustness of the theoretical trends across different modeling platforms underscores electrostatics as a significant component in the diverse roles of ATP in the context of its well-documented ability to modulate biomolecular LLPS via hydrophobic and π-related effects [63, 65, 67].”

Here, the last sentence quoted above addresses this reviewer’s question about our intended meaning in referring to “diverse roles of ATP” in the title of our paper. To make this point even clearer, we have also added the following sentence to the Abstract (p. 2, lines 12–13):

“... The electrostatic nature of these features complements ATP’s involvement in π-related interactions and as an amphiphilic hydrotrope, ...”

Moreover, to enhance readability, we have now added pointers in the rG-RPA part of our paper to anticipate the structurally more complex ATP and ATP-Mg models to be introduced subsequently in the FTS part, as follows:

p. 9 (lines 13–15):

“As mentioned above, in the present rG-RPA formulation, (ATP-Mg)²⁻ and ATP⁴⁻ are modeled minimally as a single-bead ion. They are represented by charged polymer models with more structural complexity in the FTS models below.”

p. 11 (lines 8–11):

These observations from analytical theory will be corroborated by FTS below with the introduction of structurally more realistic models of (ATP-Mg) ²⁻, ATP⁴⁻ together with the possibility of simultaneous inclusion of Na⁺, Cl−, and Mg²⁺ in the FTS models of Caprin1/pY-Caprin1 LLPS systems.

Reviewer #2 (Public Review):

Summary:

In this paper, Lin and colleagues aim to understand the role of different salts on the phase behavior of a model protein of significant biological interest, Caprin1, and its phosphorylated variant, pY-Caprin1. To achieve this, the authors employed a variety of methods to complement experimental studies and obtain a molecular-level understanding of ion partitioning inside biomolecular condensates. A simple theory based on rG-RPA is shown to capture the different salt dependencies of Caprin1 and pY-Caprin1 phase separation, demonstrating excellent agreement with experimental results. The application of this theory to multivalent ions reveals many interesting features with the help of multicomponent phase diagrams. Additionally, the use of CG model-based MD simulations and FTS provides further clarity on how counterions can stabilize condensed phases.

Strengths:

The greatest strength of this study lies in the integration of various methods to obtain complementary information on thermodynamic phase diagrams and the molecular details of the phase separation process. The authors have also extended their previously proposed theoretical approaches, which should be of significant interest to other researchers. Some of the findings reported in this paper, such as bridging interactions, are likely to inspire new studies using higher-resolution atomistic MD simulations.

Weaknesses:

The paper does not have any major issues.

We are very encouraged by this reviewer’s positive assessment of our work.

Reviewer #3 (Public Review):

Authors first use rG-RPA to reproduce two observed trends. Caprin1 does not phase separate at very low salt but then undergoes LLPS with added salt while further addition of salt reduces its propensity to LLPS. On the other hand pY-Caprin1 exhibits a monotonic trend where the propensity to phase separate decreases with the addition of salt. This distinction is captured by a two component model and also when salt ions are explicitly modeled as a separate species with a ternary phase diagram. The predicted ternary diagrams (when co and counter ions are explicitly accounted for) also predict the tendency of ions to co-condense or exclude proteins in the dense phase. Predicted trends are generally in line with the measurement for Cparin1 [sic]. Next, the authors seek to explain the observed difference in phase separation when Arginines are replaced by Lysines creating different variants. In the current rG-RPA type models both Arginine (R) and Lysine (K) are treated equally since non-electrostatic effects are only modeled in a meanfield manner that can be fitted but not predicted. For this reason, coarse grain MD simulation is suitable. Moreover, MD simulation affords structural features of the condensates. They used a force field that is capable of discriminating R and K. The MD predicted degrees of LLPS of these variants again is consistent with the measurement. One additional insight emerges from MD simulations that a negative ion can form a bridge between two positively charged residues on the chain. These insights are not possible to derive from rG-RPA. Both rG-RPA and MD simulation become cumbersome when considering multiple types of ions such as Na, Cl, [ATP] and [ATP-Mg] all present at the same time. FTS is well suited to handle this complexity. FTS also provides insights into the co-localization of ions and proteins that is consistent with NMR. By using different combinations of ions they confirm the robustness of the prediction that Caprin1 shows salt-dependent reentrant behavior, adding further support that the differential behavior of Caprin1, and pY-Caprin1 is likely to be mediated by charge-charge interactions.

We are encouraged by this reviewer’s positive assessment of our manuscript.

Reviewer #1 (Recommendations For The Authors):

Analysis:

Analyze the simulation results to distinguish net charge neutralization and interchain bridging; see MacAinsh et al.

Please see response above to points (3) and (4) under “Weaknesses” in this reviewer’s public review. We have now added a 1.5-page subsection starting from the bottom of p. 12 to the top of p. 14 to discuss a new extensive analysis of Arg⁺–Cl⁻–Arg⁺ configurations to identify bridging interactions, with key results reported in a new Fig. 6 (p. 42). Recent results from MacAinsh, Dey & Zhou (cited now as ref. 72) are included in the added discussion. Relevant advances made in MacAinsh et al., including clarification and classification of salt-mediated interactions in the phase separation of A1-LCD are now mentioned multiple times in the revised manuscript (p. 5, lines 19–20; p. 6, lines 2–5; p. 11, line 30; p. 14, line 10; p. 18, lines 28–29; and p. 20, line 4).

Writing and presentation

(1) Cite subtle effects that may be missed by the coarser approaches in this study

Please see response above to point (1) under “Weaknesses” in this reviewer’s public review.

(2) Try to distill the findings into a simple set of conclusions

Please see response above to point (2) under “Weaknesses” in this reviewer’s public review.

(3) Clarify and simplify physical interpretations

Please see response above to point (2) under “Weaknesses” in this reviewer’s public review.

(4) Explain the treatment of ATP-Mg as either a single ion or two separate ions; reconsider modifying the reference to ATP in the title

Please see response above to point (4) under “Weaknesses” in this reviewer’s public review.

(5) Minor points:

p. 4, citation of ref 56: this work shows ATP is a driver of LLPS, not merely a regulator (promotor or suppressor)

This citation to original ref. 56 (now ref. 63) on p. 4 is now corrected (bottom line of p. 4).

p. 7 and throughout: “using bulk [Caprin1]” – I assume this is the initial overall Caprin1 concentration. It would avoid confusion to state such concentrations as “initial” or “initial overall”

We have now added “initial overall concentration” in parentheses on p. 8 (line 4) to clarify the meaning of “bulk concentration”.

p. 7 and throughout: both mM (also uM) and mg/ml have been used as units of protein concentration and that can cause confusion. Indeed, the authors seem to have confused themselves on p. 9, where 400 (750) mM is probably 400 (750) mg/ml. The same with the use of mM and M for salt concentrations (400 mM Mg2+ but 0.1 and 1.0 M Na+)

Concentrations are now given in both molarity and mass density in Fig. 1 (p. 37), Fig. 2 (p. 38), Fig. 4 (p. 40), and Fig. 7 (p. 43), as noted in the text on p. 8 (lines 4–5). Inconsistencies and errors in quoting concentrations are now corrected (p. 10, line 18, and p. 11, line 2).

p. 7, “LCST-like”: isn’t this more like a case of a closed coexistence curve that contains both UCST and LCST?

The discussion on p. 8 around this observation from Fig. 1d is now expanded, including alluding to the theoretical possibility of a closed co-existence curve mentioned by this reviewer, as follows:

“Interestingly, the decrease in some of the condensed-phase [pY-Caprin1]s with decreasing T (orange and green symbols for ≲ 20◦C in Fig. 1d trending toward slightly lower [pY-Caprin1]) may suggest a hydrophobicity-driven lower critical solution temperature (LCST)-like reduction of LLPS propensity as temperature approaches ∼ 0◦C as in cold denaturation of globular proteins [7,23] though the hypothetical LCST is below 0◦C and therefore not experimentally accessible. If that is the case, the LLPS region would resemble those with both an UCST and a LCST [4]. As far as simple modeling is concerned, such a feature may be captured by a FH model wherein interchain contacts are favored by entropy at intermediate to low temperatures and by enthalpy at high temperatures, thus entailing a heat capacity contribution in χ(T), with [7,109,110] beyond the temperature-independent ϵ_h and ϵ_s used in Fig. 1c,d and Fig. 2. Alternatively, a reduction in overall condensed-phase concentration can also be caused by formation of heterogeneous locally organized structures with large voids at low temperatures even when interchain interactions are purely enthalpic (Fig. 4 of ref. [111]).”

p. 8 “Caprin1 can undergo LLPS without the monovalent salt (Na+) ions (LLPS regions extend to [Na+] = 0 in Fig. 2e,f”: I don’t quite understand what’s going on here. Is the effect caused by a small amount of counterion (ATP-Mg) that’s calculated according to eq 1 (with z s set to 0)?

The discussion of this result in Fig. 2e,f is now clarified as follows (p. 10, lines 8–14 in the revised manuscript):

“The corresponding rG-RPA results (Fig. 2e–h) indicate that, in the present of divalent counterions (needed for overall electric neutrality of the Caprin1 solution), Caprin1 can undergo LLPS without the monvalent salt (Na+) ions (LLPS regions extend to [Na+] = 0 in Fig. 2e,f; i.e., ρs \= 0, ρc > 0 in Eq. (1)), because the configurational entropic cost of concentrating counterions in the Caprin1 condensed phase is lesser for divalent (zc \= 2) than for monovalent (zc \= 1) counterions as only half of the former are needed for approximate electric neutrality in the condensed phase.”

p. 9 “Despite the tendency for polymer field theories to overestimate LLPS propensity and condensed-phase concentrations”: these limitations should be mentioned earlier, along with the very high concentrations (e.g., 1200 mg/ml) in Fig. 2

This sentence (now on p. 11, lines 11–18) is now modified to clarify the intended meaning as suggested by this reviewer:

“Despite the tendency for polymer field theories to overestimate LLPS propensity and condensed-phase concentrations quantitatively because they do not account for ion condensation [99]—which can be severe for small ions with more than ±1 charge valencies as in the case of condensed [Caprin1] ≳ 120 mM in Fig. 2i–l, our present rG-RPA-predicted semi-quantitative trends are consistent with experiments indicating “

In addition, this limitation of polymer field theories is also mentioned earlier in the text on p. 6, lines 30–31.

Reviewer #2 (Recommendations For The Authors):

(1) he current version of the paper goes through many different methodologies, but how these methods complement or overlap in terms of their applicability to the problem at hand may not be so clear. This can be especially difficult for readers not well-versed in these methods. I suggest the authors summarize this somewhere in the paper.

As mentioned above in response to Reviewer #1, we have now added a subsection with heading “Overview of key observations from complementary approaches” at the beginning of the “Results” section on p. 6 (lines 18–37) and the first line of p. 7 to make our paper more accessible to readers who might not be well-versed in the various theoretical and computational techniques. A few sentences to summarize our key results are added as well to the first paragraph of “Discussion” (p. 18, lines 23–26).

(2) It wasn’t clear if the authors obtained LCST-type behavior in Figure 1d or if another phenomenon is responsible for the non-monotonic change in dense phase concentrations. At the very least, the authors should comment on the possibility of observing LCST behavior using the rG-RPA model and if modifications are needed to make the theory more appropriate for capturing LCST.

As mentioned above in response to Reviewer #1, the discussion regarding possible LCSTtype behanvior in Fig. 1d is now expanded to include two possible physical origins: (i) hydrophobicity-like temperature-dependent effective interactions, and (ii) formation of heterogeneous, more open structures in the condensed phase at low temperatures. Three additional references [109, 110, 111] (from the Dill, Chan, and Panagiotopoulos group respectively) are now included to support the expanded discussion. Again, the modified discussion is as follows:

“Interestingly, the decrease in some of the condensed-phase [pY-Caprin1]s with decreasing T (orange and green symbols for ≲ 20◦C in Fig. 1d trending toward slightly lower [pY-Caprin1]) may suggest a hydrophobicity-driven lower critical solution temperature (LCST)-like reduction of LLPS propensity as temperature approaches ∼ 0◦C as in cold denaturation of globular proteins [7,23] though the hypothetical LCST is below 0◦C and therefore not experimentally accessible. If that is the case, the LLPS region would resemble those with both an UCST and a LCST [4]. As far as simple modeling is concerned, such a feature may be captured by a FH model wherein interchain contacts are favored by entropy at intermediate to low temperatures and by enthalpy at high temperatures, thus entailing a heat capacity contribution in χ(T), with [7,109,110] beyond the temperature-independent ϵ_h and ϵ_s used in Fig. 1c,d and Fig. 2. Alternatively, a reduction in overall condensed-phase concentration can also be caused by formation of heterogeneous locally organized structures with large voids at low temperatures even when interchain interactions are purely enthalpic (Fig. 4 of ref. [111]).”

(3) In Figures 4c and 4d, ionic density profiles could be shown as a separate zoomed-in version to make it easier to see the results.

This is an excellent suggestion. Two such panels are now added to Fig. 4 (p. 40) as parts (g) and (h).

Reviewer #3 (Recommendations For The Authors):

I would suggest authors make some minor edits as noted here.

(1) Please note down the chi values that were used when fitting experimental phase diagrams with rG-RPA theory in Figure 2a,b. At present there aren’t too many such values available in the literature and reporting these would help to get an estimate of effective chi values when electrostatics is appropriately modeled using rG-RPA.

The χ(T) values and their enthalpic and entropic components ϵh and ϵs used to fit the experimental data in Fig. 1c,d are now stated in the caption for Fig. 1 (p. 37). Same fitted χ(T) values are used in Fig. 2 (p. 38) as it is now stated in the revised caption for Fig. 2. Please note that for clarity we have now changed the notation from ∆h and ∆s in our originally submitted manuscript to ϵh and ϵs in the revised text (p. 7, last line) as well as in the revised figure captions to conform to the notation in our previous works [18, 71].

(2) Authors note “monovalent positive salt ions such as Na+ can be attracted, somewhat counterintuitively, into biomolecular condensates scaffolded by positively-charged polyelectrolytic IDRs in the presence of divalent counterions”. This may be due to the fact that the divalent negative counterions present in the dense phase (as seen in the ternary phase diagrams) also recruit a small amount of Na+.

The reviewer’s comment is valid, as a physical explanation for this prediction is called for. Accordingly, the following sentence is added to p. 10, lines 27–29:

“This phenomenon arises because the positively charge monovalent salt ions are attracted to the negatively charged divalent counterions in the protein-condensed phase.”

(3) In the discussion where authors contrast the LLPS propensity of Caprin1 against FUS, TDP43, Brd4, etc, they correctly note majority of these other proteins have low net charge and possibly higher non-electrostatic interaction that can promote LLPS at room temperature even in the absence of salt. It is also worth noting if some of these proteins were forced to undergo LLPS with crowding which is sometimes typical. A quick literature search will make this clear.

A careful reading of the work in question (Krainer et al., ref. 50) does not suggest that crowders were used to promote LLPS for the proteins the authors studied. Nonetheless, the reviewer’s point regarding the potential importance of crowder effects is well taken. Accordingly, crowder effects are now mentioned briefly in the Introduction (p. 4, line 13), with three additional references on the impact of crowding on LLPS added [30–32] (from the Spruijt, Mukherjee, and Rakshit groups respectively). In this connection, to provide a broader historical context to the introductory discussion of electrostatics effects in biomolecular processes in general, two additional influential reviews (from the Honig and Zhou groups respectively) are now cited as well [15, 16].

eLife Assessment

This manuscript aims to identify the pacemaker cells in the lymphatic collecting vessels - the cells that initiate the autonomous action potentials and contractions needed to drive lymphatic pumping. Through the exemplary use of existing approaches (genetic deletions and cytosolic calcium detection in multiple cell types), the authors convincingly determine that lymphatic muscle cells are the origin of the action potential that triggers lymphatic contraction. The inclusion of scRNAseq and membrane potential data enhances a tremendous study. This fundamental discovery establishes a new standard for the field of lymphatic physiology.

Reviewer #1 (Public review):

Summary:

This manuscript explores the multiple cell types present in the wall of murine collecting lymphatic vessels with the goal of identifying cells that initiate the autonomous action potentials and contractions needed to drive lymphatic pumping. Through the use of genetic models to delete individual genes or detect cytosolic calcium in specific cell types, the authors convincingly determine that lymphatic muscle cells are the origin of the action potential that triggers lymphatic contraction.

Strengths:

The experiments are rigorously performed, the data justify the conclusions and the limitations of the study are appropriately discussed.

There is a need to identify therapeutic targets to improve lymphatic contraction and this work helps identify lymphatic muscle cells as potential cellular targets for intervention.

Reviewer #2 (Public review):

Summary:

This is a well written manuscript describing studies directed at identifying the cell type responsible for pacemaking in murine collecting lymphatics. Using state-of-the-art approaches, the authors identified a number of different cell types in the wall of these lymphatics and then using targeted expression of Channel Rhodopsin and GCaMP, the authors convincingly demonstrate that only activation of lymphatic muscle cells produces coordinated lymphatic contraction and that only lymphatic muscle cells display pressure-dependent Ca2+ transients as would be expected of a pacemaker in these lymphatics.

Strengths:

The use of targeted expression of channel rhodopsin and GCaMP to test the hypothesis that lymphatic muscle cells serve as the pacemakers in musing lymphatic collecting vessels.

Weaknesses:

The only significant weakness was the lack of quantitative analysis of most of the imaging data shown in Figures 1-11. In particular, the colonization analysis should be extended to show cells not expected to demonstrate colocalization as a negative control for the colocalization analysis that the authors present. These weaknesses have been resolved by revision and addition of new and novel RNAseq data, additional colocalization data and membrane potential measurements.

Reviewer #3 (Public review):

Summary:

Zawieja et al. aimed to identify the pacemaker cells in the lymphatic collecting vessels. Authors have used various Cre-based expression systems and optogentic tools to identify these cells. Their findings suggest these cells are lymphatic muscle cells that drive the pacemaker activity in the lymphatic collecting vessels.

Strengths:

The authors have used multiple approaches to test their hypothesis. Some findings are presented as qualitative images, while some quantitative measurements are provided.

Weaknesses:

- More quantitative measurements.<br /> - Possible mechanisms associated with the pacemaker activity.<br /> - Membrane potential measurements.

Comments on revisions:

The authors have answered my comments with additional experiments, data and manuscript edits.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

This manuscript explores the multiple cell types present in the wall of murine-collecting lymphatic vessels with the goal of identifying cells that initiate the autonomous action potentials and contractions needed to drive lymphatic pumping. Through the use of genetic models to delete individual genes or detect cytosolic calcium in specific cell types, the authors convincingly determine that lymphatic muscle cells are the origin of the action potential that triggers lymphatic contraction. 

Strengths: 

The experiments are rigorously performed, the data justify the conclusions, and the limitations of the study are appropriately discussed. 

There is a need to identify therapeutic targets to improve lymphatic contraction and this work helps identify lymphatic muscle cells as potential cellular targets for intervention. 

Weaknesses: 

My only major comment would be that the manuscript provides a lot of rich information describing the cellular components of the muscular lymphatic vessel wall and that these data are not well represented by the title. The title (while currently accurate) could be tweaked to better represent all that is in this manuscript. Maybe something like

"Characterization/Interrogation of the cellular components of murine collecting lymphatic vessels reveals that lymphatic muscle cells are the innate pacemaker cells regulating lymphatic contractions" or "Discovery/Confirmation of lymphatic muscle cells as innate pacemaker cells of lymphatic contraction through characterization of the cellular components of murine collecting lymphatic vessels". Potentially a cartoon summary figure of the components that make up the collecting lymphatic vessel wall could also be included. In my opinion, these changes will make this manuscript of more interest to a broader group of scientists. I have a few additional comments for consideration to improve the clarity and enhance the discussion of this work. 

We agree with the reviewer that our original manuscript, and our resubmission even more so with the addition of the scRNAseq data, provides a significant amount of information regarding the composition of the lymphatic collecting vessel wall. We have changed our title to match one suggestion of the reviewer: “Characterization of the cellular components of murine collecting lymphatic vessels reveals that lymphatic muscle cells are the innate pacemaker cells regulating lymphatic contractions".

Reviewer #2 (Public Review): 

Summary: 

This is a well-written manuscript describing studies directed at identifying the cell type responsible for pacemaking in murine-collecting lymphatics. Using state-of-the-art approaches, the authors identified a number of different cell types in the wall of these lymphatics and then using targeted expression of Channel Rhodopsin and GCaMP, the authors convincingly demonstrate that only activation of lymphatic muscle cells produces coordinated lymphatic contraction and that only lymphatic muscle cells display pressure-dependent Ca2+ transients as would be expected of a pacemaker in these lymphatics. 

Strengths: 

The use of a targeted expression of channel rhodopsin and GCaMP to test the hypothesis that lymphatic muscle cells serve as the pacemakers in musing lymphatic collecting vessels. 

Weaknesses: 

The only significant weakness was the lack of quantitative analysis of most of the imaging data shown in Figures 1-11. In particular, the colonization analysis should be extended to show cells not expected to demonstrate colocalization as a negative control for the colocalization analysis that the authors present. 

We understand the reviewer’s concern regarding the lack of a control for the colocalization analysis and that the colocalization analysis was limited to just one set of cell markers. We have now provided a colocalization analysis of Myh11 and PDGFRα, to serve as a co-localization negative control based on our RT-PCR and scRNASeq findings, which is incorporated into the current Supplemental figure 1. In regard to the staining pattern of other various marker combinations, the results were often quite clear with the representative images that two separate cell populations were being stained such as the case with labeling endothelial cells with CD31, macrophage labeling with the MacGreen mice, or hematopoietic cells with CD45. 

During our lengthy rebuttal process we completed a single cell RNA sequence analysis using our isolated and cleaned mouse inguinal axillary lymphatic collecting vessels to aid in our characterization of the vessel wall and to more thoroughly answer these questions regarding colocalization in arguably a robust manner. The generation of our scRNAseq dataset, derived from isolated and cleaned mouse inguinal axillary collecting vessels from 10 mice, 5 male and 5 females, allowed us to profile over 2200 of the adventitial fibroblast like cells (AdvCs) we had identified in our original submission. Using this dataset, we were able to confirm co-expression of Cd34 and Pdgfrα in AdvCs and assess the co-expression of other genes of interest from our RT-PCR experiments and immunofluorescence experiments. This approach will also allow other lymphatic investigators to assess their genes of interest as our dataset is uploaded to the NIH Gene Omnibus and will be uploaded to the Broad Institute Single Cell Portal upon publication.

Here we show that the vast majority of non-muscle fibroblast like cells referred to as AdvCs were double positive for both CD34 and PDGFRα. We also show that the AdvCs that express commonly used pericyte markers Pdgfrb and Cspg4 also co-expressed Pdgfrα. Critically, this data also shows that the AdvCs that express genes linked with lymphatic contractile dysfunction (Ano1, Gjc1 or connexin 45, and Cacna1c “Cav1.2”) co-express Pdgfrα and would render these genes susceptible to Cre-mediated recombination using our Pdgfrα-CreER^TM model.  

Reviewer #3 (Public Review): 

Summary: 

Zawieja et al. aimed to identify the pacemaker cells in the lymphatic collecting vessels. Authors have used various Cre-based expression systems and optogenetic tools to identify these cells. Their findings suggest these cells are lymphatic muscle cells that drive the pacemaker activity in the lymphatic collecting vessels. 

Strengths: 

The authors have used multiple approaches to test their hypothesis. Some findings are presented as qualitative images, while some quantitative measurements are provided.   

Weaknesses: 

-  More quantitative measurements. 

-  Possible mechanisms associated with the pacemaker activity. 

-  Membrane potential measurements. 

We thank the reviewers for their concerns and have addressed them in the following manner. 

- We added novel single cell RNA sequencing of isolated and cleaned inguinal axillary vessels from 10 mice (5 males and 5 females). This allowed us to quantify the number of AdvCs that coexpress CD34 and Pdgfrα as well as the number of cells co-expressing Pdgfrα and other markers.

- We have added a negative control with quantification for the co-localization analysis assessing Myh11 and Pdgfrα. We have added a negative control with quantification for the ChR2-photo stimulated contraction experiments using Myh11CreERT2-ChR2 mice that were not injected with tamoxifen. 

- We also used Biocytin-AF488 in our intracellular Vm electrodes to map the specific cells in which we recorded action potentials and in neighboring cells since Biocytin-AF488 is under 1KDa and can pass through gap junctions. This approach independently labeled lymphatic muscle cells and their direct neighbors for 3 IALVs from 3 separate mice. 

- We performed membrane potential recordings in isolated, pressurized (under isobaric conditions), and spontaneously contracting inguinal axillary lymphatic collecting vessels at different pressures. 

- We also show that the pressure-frequency relationship is dependent on the slope of the diastolic depolarization as no other parameter was significantly altered in our study and the diastolic depolarization slope was highly correlated with contraction frequency. 

We believe the addition of these novel data, controls, experiments, and quantifications have improved the manuscript in line with the reviewers’ suggestions.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

Lines 149-162: The authors rule out the methylene blue staining cells in the cLV wall as pacemakers because they don't form continuous longitudinal connections to drive propagation. Is it possible for a pacemaker cell to only initiate the contraction and then have the LMCs make the axial electrical connections to propagate the electrical wave? I am not trying to suggest the methylene blue cells are pacemakers, but I am not sure the lack of longitudinal (or radial) connectivity is sufficient evidence to rule out the possibility. This comment also is relevant to the 3 criteria for a pacemaker cell listed in the Discussion (Lines 413-417). 

We agree with the reviewer’s broader point that a pacemaker cell may not require direct contact with other ‘pacemaker’ cells within the tissue as long as they are still within the same electrical syncytium. However, we do expect a continuous presence of a pacemaker cell type throughout the vessel wall length to account for the persistence of spontaneous contractile behavior despite vessel length, and the ability for contraction initiation to shift (Akl et al 2011, Castorena et al 2018 and Castorena et al 2022) and the occurrence of spontaneous action potentials. In Dirk van Helden’s seminal work in 1993 on lymphatic pacemaking, a major finding was that “SM of small lymphangions or that of short segments, cut from lymphangions of any length, behaved similarly”. We have adjusted our phrase regarding the requirement of a contiguous network and instead suggest a continuous presence along the vessel network and integrated into the electrical syncytium. 

Methylene blue is an alkaline stain that will stain acidic structures and historically methylene blue is noted to stain Interstitial cells of Cajal in the gastrointestinal tract which typically exist as network of cells(Huizinga et al 1993 and Berezin 1988). No such network was readily apparent in our methylene blue staining nor did the stained cells have a similar morphology to the ICCs of the gastrointestinal tract. Further, methylene blue is staining is not limited to ICCs or pacemaker cells at large as it has been used to kill cancer cells. Within the small intestine methylene blue was noted to also stain macrophage like cells (Mikkelsen et al 1988), and we too draw parallels between the macrophage morphology observed with Macgreen mice and methylene-blue stained cells. The specific structure for the ICC affinity for methylene blue is not well described and while the innate cytotoxicity of methylene blue and light has been used to kill ICCs and impair slow wave generation, the lack of specificity of this method leaves much to be desired. What is clear is that the ICC network highlighted by methylene blue in the gut is absent in lymphatic collecting vessels.

In Figure 15/Video12, is it possible that the cells that are showing intracellular Ca2+ in diastole are the cells that reach a threshold membrane potential that then trigger the rest of the LMCs? As the authors have shown heterogeneity in the LMCs surface markers, is it possible that the cells with Ca2+ activity during diastole are identifiable by a distinct molecular phenotype? Or is the thought that these cells are randomly active in diastole? Some discussion/speculation about this seems appropriate. 

We are in agreement with the reviewer’s conclusion that there is heterogeneity in the LMCs as it pertains to the calcium oscillations in diastole, either under normal buffer conditions or when L-type channels are inhibited with nifedipine. We also note significant heterogeneity in the gene expression noted within the four LMC subclusters (0-3), though we did not see significant differences in either in Ip3R1 or Ano1 expression. However, subcluster “0” had increased expression of Itprid2, also known as KRas-induced actin-interacting protein (KRAP) which is thought to tether, and thus immobilize, IP3 receptors to the actin cortex beneath the cell membrane. KRAP has been recently proposed to be a critical player in IP3 receptor “licensing” which allows IP3 receptors to release calcium (Vorontsova et al., 2022).  However, whether similar requirement of IP3R licensing is necessitated in all cells or specifically in LMCs is unknown it is quite clear there are specific release sites within the cell and this topic is currently under further investigation for a separate manuscript. We would like to note that there is yet to be a clear consensus on whether IP3R licensing is required as much of these studies are performed in cultured cells and this mechanism has only recently been described. 

Healthy lymphatic collecting vessels typically have a single pacemaker driving a coordinated propagated contraction in ex vivo isobaric myograph studies (Castorena-Gonzalez et al., 2018), which is typically at either end of the cannulated vessel. We believe that this is due to the lack of a bordering cell in one direction and allows charge to accumulate and voltage to reach threshold at these sites preferentially. We have tried to image calcium at the pacemaking pole of the vessel to observe the specific Ca²⁺ transients at these sites though invariably the act of imaging GCaMP6f results in the pacemaker activity initiating from the other pole of the vessel. It is our opinion that the fact that LMCs are heterogenous in their Ca²⁺ transients is a feature to the system as it permits a wider range of depolarization signals, and thus allows finer control of the pacing as different physical/pressure or signaling stimuli is encountered. Likely, the cells with the higher propensity of Ca²⁺ transients act as the contraction initiation site in vivo, though it must also be noted that the LMC density decreases around lymphatic valve sites. In fact, in guinea pig collecting vessels there are very few LMCs at the valves which can render them electrically uncoupled or poorly coupled (Van Helden, 1993). Thus, valve sites in which there is greater electrical resistance due to lower LMC-LMC coupling may allow for charge accumulation in the LMCs at the valve site, similar to the artificial condition achieved in our myograph preparations with two cut ends, and allow them to reach threshold first and drive coordination at the valve sties.

An additional description of what the PTCL analysis is meant to represent physiologically would be helpful for readers. 

We have better described the conversion of the calcium signals into “particles” for analysis at first mention in the methods and results section and have included the requisite reference to this specific methodology in Line 429-30. 

A description of how DMAX is experimentally determined is needed. 

We have adjusted our methods section to describe DMAX in line 774-775.

“with Ca²⁺-free Krebs buffer (3mM EGTA) and diameter at each pressure recorded under passive conditions (DMAX).”

I think the vessels referred to as popliteal lymphatic vessels are actually saphenous lymphatic vessels (afferent to the popliteal lymph node). Please clarify. 

Indeed, some of the vessels used in this study are the afferents to the single popliteal node. They travel with the caudal branch of the saphenous vein, but have routinely been described as popliteal vessels, as opposed to saphenous lymphatic vessels, by the lymphatic field at large (Tilney 1971 PMCID: PMC1270981, Liao 2015 PMID: 25512945). To move away from this nomenclature would likely add to confusion although we agree that the lymphatic field may need to improve or correct the vessel naming paradigm to match the vascular pairs they follow.

Reviewer #2 (Recommendations For The Authors): 

Lines 214-215 - can you cite a reference for the observation that rhythmic contractions don't require the presence of valves? 

We have added the reference. In Dr. Van Helden’s seminal work on the topic in 1993, “Vessel segments were then cut from selected small lymphangions (length 300-500 um) by cutting at the valves.” Additionally, work by Dr Anatoliy Gashev utilized sections of lymphatic vessels that lacked valves to study orthograde and retrograde shear sensitivity (Gashev et al., 2002).

Lines 224-230 - It would have been nice to see colocalization analysis for all cell types so that "negative" results could be compared with the "positives" that you report. This would help bolster evidence of your ability to separate cell types. 

We understand the reviewer’s sentiment and agree. We have now added a “negative control” colocalization staining and analysis for PDGFR and Myh11 which has been added to the current SuppFigure 1. We stained 3 IALVs from 3 separate mice with PDGFRα and Myh11 and performed confocal microscopy. We ran the FIJI BIOP-JACOP colocalization plugin as before and observed very little colocalization of the two signals. Additionally, we have also added a coexpression assessment for CD34 and PDGFRα and other genes using our scRNAseq dataset.  

line 293 - Should read "Cx45 in..." 

This has been corrected. 

“The expression of the genes critically involved in cLV function—Cav1.2, Ano1, and Cx45—in the PdgfrαCreER^TM-ROSA26mTmG purified cells and scRNAseq data prompted us to generate PdgfrαCreER^TM-Ano1^fl/fl, PdgfrαCreER^TM-Cx45^fl/fl, and PdgfrαCreER^TM-Cav1.2^fl/fl mice for contractile tests.”

lines 470-473 - A reference for this statement should be cited. 

We have added the reference. In Dr. Van Helden’s seminal work on the topic in 1993, “Vessel segments were then cut from selected small lymphangions (length 300-500 um) by cutting at the valves.” Additionally, work by Dr Anatoliy Gashev utilized sections of lymphatic vessels that lacked valves to study orthograde and retrograde shear sensitivity (Gashev et al., 2002).

Lines 483-487 - References should be cited for these statements. 

We have narrowed and clarified this statement and supported it with the necessary citations. 

“Of course, mesenchymal stromal cells (Andrzejewska et al., 2019) and fibroblasts (Muhl et al., 2020; Buechler et al., 2021; Forte et al., 2022) are present, and it remains controversial to what extent telocytes are distinct from or are components/subtypes of either cell type (Clayton et al., 2022). Telocytes are not monolithic in their expression patterns, displaying both organ directed transcriptional patterns as well as intra-organ heterogeneity (Lendahl et al., 2022) as readily demonstrated by recent single cell RNA sequencing studies that provided immense detail about the subtypes and activation spectrum within these cells and their plasticity (Luo et al., 2022).”

Lines 584-585 - Missing a reference citation. 

Thank you for catching this error, the correct citation was for Boedtkjer et al 2013 and is now properly cited. 

Line 638 - "these this" should read "this" 

Thank you for catching this error. This particular sentence was removed in light of the addition of the scRNAseq data.

Reviewer #3 (Recommendations For The Authors): 

This manuscript from Zawieja et al. explored an interesting hypothesis about the pacemaker cells in lymphatic collecting vessels. Many aspects of lymphatic collecting vessels are still under investigation; hence this work provides timely knowledge about the lymphatic muscle cells as a pacemaker. Although it is an important topic of the investigation, the data provided do not support the overall goal of the manuscript. Many figures (Figure 1-5) provide quantitative estimation and the description provided in the results section might only be useful for a restricted audience, but not to the broader audience. Some of the figures are very condensed with multiple imaging panels and it is hard to follow the differences in qualitative analysis. Overall, this manuscript can be improved by more streamlined description/writing and figure arrangements (some of the figures/panels can be moved to the supplementary figures). 

We disagree with the notion that the original data provided did not support the goal of the manuscript- to identify and test putative pacemaker cell types. Nonetheless we believe we have also added ample novel data to the manuscript, including membrane potential recordings and scRNAseq to highlight and to add further support to our conclusion that the pacemaker cell is an LMC. We believe the scRNAseq data will also greatly enhance the appeal of the manuscript to a broader audience and have renamed the manuscript in line with the wealth of data we have collected on the components of the vessel wall as we tested for putative pacemaker cells.

As requested, we have moved many figures to the supplement to allow readers to focus more on the more critical experiments.

A few other points that need to be addressed: 

(1) Authors used immunofluorescence-based differences in various cell types in the collecting vessels. Initially, they chose ICLC, pericytes, and lymphatic muscle cells. But then they started following adventitial cells and endothelial cells. It is not clear from the description, why these other cells could be possibly involved in the pacemaker activity. It will be easier to follow if authors provide a graphical abstract or summary figure about their hypothesis and what is known from their and others' work. 

We would like to clarify that we used the endothelial cells as controls to ensure what we observed via immunofluorescence and FACs RT-PCR were a separate cell type from either lymphatic muscle or lymphatic endothelial cells on the vessel wall. Staining for the endothelium also allowed us to assess where these PDGFRα+CD34+ cells reside in the vessel wall.  We started with a wide range of markers that are conventionally used for targeting specific cell types, but as expected those markers are not always 100% specific. Specifically, we focused on CD34, Kit, and Vimentin as those were the markers for the non-muscle cells observed in the lymphatic collecting vessel wall previously. What we found was that CD34 and PDGFRα labeled the same cell type. As there was not a CD34Cre mouse available at the time we instead utilized the inducible PDGFRαCreERTM. We are unsure how well an abstract figure will condense the conclusions from the experiments listed here but if absolutely required for publication we can attempt to highlight the representative cell populations identified on the vessel wall.

(2) Authors used many acronyms in the manuscript without defining them (when they appeared for the first time). Please follow the convention. 

We have checked the manuscript and made several corrections regarding the use of abbreviations.

(3) How specific PDGFR-alpha as a marker of the pericytes? It can also label the mesenchymal cells. Why did the author choose PDGFR-alpha over beta for their Cre-based expression approach? 

We tried to assess if there were a pericyte like cell present in or along the wall using PDGFRbeta (Pdgfrβ). Pdgfrβ is commonly used to identify pericytes (Winkler et al., 2010), while in contrast Pdgfrα is a known fibroblast marker (Lendahl et al., 2022). Pdgfrβ CreERT2 resulted in recombination in both LMCs and AdvCs, preventing it from being a discriminating marker for our study where as Myh11CreER^T2 and PDGFRαCreER^TM were specific at least to cell type based on our FACSs-RT-PCR and staining. As you can tell from the scRNAseq data in Figure 5, there was no cell cluster that Pdgfrβ was specific for in contrast to PDGFRα and Myh11.  In Figure 6 we show the expression of another commonly used pericyte marker NG2 (Cspg4) in our scRNAseq dataset which was observed in both LMCs and AdvCs as well. Lastly, MCAM (Figure 6) can also be a marker for pericytes though we see only expression in the LMCs and LECs for this marker. Notably, almost all of the AdvCs express PDGFRα rendering the PDGFRαCreER^TM a powerful tool to study this population of cells on the vessel wall including those that were PDGFRα+Cspg4+ or PDGFRα+ Pdgfrβ+.

We were reliant on PDGFRαCreER^TM as that was the only available PDGFRα Cre model at the time. Note we used PdgfrβCreER^T2 and Ng2Cre in our study but found that both Cre models recombined both LMCs and AdvCs.

(4) Please include appropriate references for all the labeling markers (PDGFR-alpha, beta, and myc11 etc.) that are used in this manuscript. 

We have added multiple references to the manuscript to support the use of these common cell “specific” markers as of course each marker is limited in some capacity to fully or specifically label a single population of cells (Muhl et al., 2020).

(5) One of the criteria for the pacemaker cells is depolarization-induced propagated contractions. Authors have used optogenetics-induced depolarization to test this phenomenon. Please include negative controls for these experiments. 

We have now added negative controls to this experiment which were non-induced (no tamoxifen) Myh11CreER^T2-Chr2 popliteal vessels. This data has been added to the Figure 8.  

(6) What are the resting membrane potentials of Lymphatic muscle cells? The authors should provide some details about this in the manuscript. 

We agree with the reviewer and have added membrane potential recordings (Figure 13) at different pressures and filled our recording electrode with the cell labeling molecule BiocytinAF488 to highlight the action potential exhibiting cells, which were the LMCs. Lymphatic resting membrane potential is dynamic in pressurized vessels, which appears to be a critical difference in this approach as compared to pinned out vessels or those on wire myographs likely due to improper stretch or damage to the vessel wall. In mesenteric lymphatic vessels isolated from rats the minimum membrane potential achieved during repolarization ranges from -45 to 50mV typically while IALVs from mice are typically around -40mV, though IALVs have a notably higher contraction frequency. Critically, we have also added novel membrane potential recordings to this manuscript in IALVs at different pressures and show that the diastolic depolarization rate is the critical factor driving the pressure-dependent frequency.

(7) In the discussion, the authors discussed SR Ca2+ cycling in Pacemaking, but the relevant data are not included in this manuscript, but a manuscript from JGP (in revision) is cross-referenced. 

As discussed above, we have recently published our work where studied IALVs from Myh11CreERT2-Ip3R1fl/fl (Ip3r1ismKO) and Myh1CreERT2-Ip3r1fl/fl-Ip3r2fl/fl-Ip3r3fl/fl mice (Zawieja et al., 2023). Deletion of Ip3r1 from LMCs recapitulated the dramatic reduction in frequency we previously published in Myh11CreERT2-Ano1fl/fl mice and the loss of pressure dependent chronotropy. Furthermore, in this manuscript we also showed that the diastolic calcium transients are nearly completely lost in ILAVs from Myh11CreERT2-Ip3R1fl/fl knockout mice. There was no difference in the contractile function between IALVs from single Ip3r1 knockout and the triple Ip3r1-3 knockout mice suggesting that it is Ip3r1 that is required for the diastolic calcium oscillations. Further, in the presence of 1uM nifedipine there were still no calcium oscillations in the Myh11CreERT2-Ip3r1fl/fl LMCs. These findings provide further support for our interpretation that the pacemaking is of myogenic origin.

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eLife Assessment

This valuable study presents new observations on white matter organisation at the micron scale, using a combination of synchrotron imaging and diffusion MRI across two species. Notably, the authors provide solid evidence for the fasciculation of axons within major fibre bundles into laminar structures, though these structures are not consistently observed across modalities or species. The study will be of general interest to neuroanatomists and those interested in white matter imaging.

Reviewer #1 (Public Review):

This study presents valuable observations of white matter organisation from diffusion MRI and two types of synchrotron imaging in both monkeys and mice. Cross-modality comparisons are interesting as the different methods are able to probe anatomical structures at different length scales, from single axons in high-resolution synchrotron (ESRF) imaging, to clusters of axons in lower-resolution synchrotron (DEXY) data, to axon populations at the mm-scale in diffusion MRI. By acquiring all modalities in monkey and mouse ex vivo samples, the authors can observe principles of fibre organisation, and characterise how fibre characteristics, such as tortuosity and micro-dispersion, vary across select brain regions and in healthy tissue versus a demyelination model.

One very interesting result is the observation of apparent laminar organisation of fibres in ex vivo monkey white matter samples. DESY data from the corpus callosum shows fibres with two dominant orientations (one L-R, one slightly inclined), clustered in laminar structures within this major fibre bundle. Thanks to the authors providing open data, I was able to look through the raw DESY volume and observe regions with different "textures" (different orientations) in the described laminar arrangement. That this organisation can be observed by eye, as well as by structure tensor, is fairly convincing.

Reviewer #2 (Public Review):

Summary:

In this work, the authors combine diffusion MRI and high-resolution x-ray synchrotron phase-contrast imaging in monkey and mouse brains to investigate the 3D organization of brain white matter across different scales and species. The work is at the forefront of the anatomical investigation of the human connectome and aligns with several current efforts to bridge the resolution gap between what we can see in vivo at the millimeter scale and the complexity of the human brain at the sub-micron scale. The authors compare the 3D white matter organization across modalities within 2 small regions in one monkey brain (body of the corpus callosum, centrum semiovale) and within one region (splenium of the corpus callosum) in healthy mice and in one murine model of focal demyelination. The study compares measures of tissue anisotropy and fiber orientations across modalities, performs a qualitative comparison of fasciculi trajectories across brain regions and tissue conditions using streamlined tractography based on the structure tensor, and attempts to quantify the shape of fasciculi trajectories by measuring the tortuosity index and the maximum deviation for each reconstructed streamline. Results show measures of anisotropy and fiber orientations largely agree across modalities, especially for larger FOV data. The high-resolution data allows us to explore the fiber trajectories in relation to tissue complexity and pathology. The authors claim the study reveals new common organization principles of white matter fibers across species and scales, for which axonal fasciculi arrange into sheet-like laminar structures.

Strengths:

The aim of the study is of central importance within present efforts to bridge the gap between macroscopic structures observable in vivo in humans using conventional diffusion MRI and the microscopic organization of white matter tissue. Results obtained from this type of study are important to interpret data obtained in vivo, inform the development of novel methodologies, and expand our knowledge of the structural and thus functional organization of brain circuits.

Multi-scale data acquired across modalities within the same sample constitute extremely valuable data that is often hard to acquire and represent a precious resource for validation of both diffusion MRI tractography and microstructure methods.

The inclusion of multi-species data adds value to the study, allowing the exploration of common organization principles across species.

The addition of data from a murine cuprizone model of focal demyelination adds interesting opportunities to study the underlying biological changes that follow demyelination and how these impact tissue anisotropy and fiber trajectories. These data can inform the interpretation and development of diffusion MRI microstructure models.

[Editors' note: The Reviewing Editor considers that the authors addressed the reviewers' questions adequately. The original reviews are here: https://elifesciences.org/reviewed-preprints/94917/reviews]

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Public Review):

This study presents valuable observations of white matter organisation from diffusion MRI and two types of synchrotron imaging in both monkeys and mice. Cross-modality comparisons are interesting as the different methods are able to probe anatomical structures at different length scales, from single axons in high-resolution synchrotron (ESRF) imaging, to clusters of axons in lower-resolution synchrotron (DEXY) data, to axon populations at the mm-scale in diffusion MRI. By acquiring all modalities in monkey and mouse ex vivo samples, the authors can observe principles of fibre organisation, and characterise how fibre characteristics, such as tortuosity and micro-dispersion, vary across select brain regions and in healthy tissue versus a demyelination model. The results are solid, though some statements (in the abstract/discussion) do not appear to be fully supported, and statistical tests would help confirm whether tissue characteristics are similar/different between different conditions.

R1.1: Thank you for the kind feedback. We have included statistical tests in the paper for tissue characteristics where appropriate.

Due to the very high number of sample points (one per voxel) within the 3D synchrotron volumes, testing for statistical significance is challenging for the structure tensor-based tissue fractional anisotropy (FA) metric. This causes any standard statistical test to have sufficient power to evaluate even minute differences between the volumes as statistically significant with high confidence. In other words, the null hypothesis (H0) will always be rejected with p = 0, regardless of the practical significance of the difference. Therefore, we have not added statistical analysis for FA results.

For the tractography based metrics, the number of sample points (one per streamline) is not as high as that for the structure tensor FA, thus making it more reasonable to test for statistical significance. The statistical analyses performed included tests for equality of distributions (Two-sample Kolmogorov-Smirnov tests), equality of medians (Two-sided Wilcoxon rank sum tests), and equality of variance (Brown-Forsythe tests). The results are described in relation to Figure 5(B, D), Figure 8(CF), and detailed in the Methods section.

One very interesting result is the observation of apparent laminar organisation of fibres in ex vivo monkey white matter samples. DESY data from the corpus callosum shows fibres with two dominant orientations (one L-R, one slightly inclined), clustered in laminar structures within this major fibre bundle. Thanks to the authors providing open data, I was able to look through the raw DESY volume and observe regions with different "textures" (different orientations) in the described laminar arrangement. That this organisation can be observed by eye, as well as by structure tensor, is fairly convincing. As not all readers will download the data themselves, the manuscript could benefit from additional figures/videos to demonstrate (1) the quality of the DESY data and (2) a more 3D visualisation of the laminar structures (where the coronal plane shows convincing columnar structure or stripes). Similarly in Figure 5A, though this nicely depicts two populations with different orientations, it is somewhat difficult to see the laminar structure in the current image.

ESRF data of the centrum semiovale (CS) contributes evidence for similar laminar structures in a crossing fibre region, where primarily AP fibres are shown to cluster in 3 laminar structures. As above, further visualisations of the ESRF volume in the CS (as shown in Figure 4E) would be of value (e.g. showing consistency across the 4 volumes, 2D images showing stripey/columnar patterns along different axes, etc).

R1.2: Conveying complex 3D geometry through 2D still images is indeed challenging, and we greatly appreciate the reviewer’s comments and suggestions. To better communicate the understanding of the 3D anatomical environments, we have taken the following actions:

(1) To enhance insights into the tractography results in Figures 5A and 5D, we have rendered and added animations of the tractography scenes as supplemental material.

(2) To visually support 3D insights concerning the consistency of the laminar organisation of the callosal fibres, we have replaced the 2D slice views in Figures 3A and 3B with 3D renderings similar to the one in Figure 4E.

(3) An animation of Figure 4E was created to display the colour-coded structure tensor directions of all four stacked scans. This animation visually supports the complexity of the fibre orientation and the layered structural laminar organisation of the CS sample.

A key limitation of this result is that, though the DESY data from the CC seems convincing, the same structures were not observed in high-resolution synchrotron (ESRF) data of the same tissue sample in the corpus callosum. This seems surprising and the manuscript does not provide a convincing explanation for this inconsistency. The authors argue that this is due to the limited FOV of the ESRF data (~200x200x800 microns). However, the observed laminar structures in DESY are ~40 microns thick, and ERSF data from the CST suggests laminar thicknesses in the range of 5-40 microns with a similar FOV. This suggests the ERSF FOV would be sufficient to capture at least a partial description of the laminar organisation. Further, the DESY data from the CC shows columnar variations along the LR axis, which we might expect to be observed along the long axis of the ESFR volume of the same sample. Additional analyses or explanations to reconcile these apparently conflicting observations would be of value. For example, the authors could consider down-sampling the ESRF data in an appropriate manner to make it more similar to the DESY data, and running the same analysis, to see if the observed differences are related to resolution (i.e. the thinner laminar structures cluster in ways that they look like a thicker laminar structure at lower resolution), or crop the DESY data to the size of the ESRF volume, to test whether the observed differences can be explained by differences in FOV. Laminar structures were not observed in mouse data, though it is unclear if this is due to anatomical differences or somewhat related to differences in data quality across species.

R1.3: We have clarified and expanded upon the results regarding the laminar organisation observed in the monkey CC DESY data. As noted in R1.2, we replaced the 2D images in Figures 3A (DESY) and 3B (ESRF) with 3D renderings to better display the spatial outline of the laminar organisation in the volumes. The reviewer is correct that, although the smaller field of view (FOV) of the ESRF data should allow us to at least partially capture parts of the laminar organisation observed in the larger FOV of the DESY data, this is not guaranteed. It depends on how the smaller FOV is positioned relative to the structural organisation, and since we lack co-registration, we do not know this. It should now be visually evident that the ESRF FOV can be placed such that it does not cover the observed laminae, a point which is now also emphasised in the Discussion. 

Secondly, it is important to emphasise that the voxel colouring using the primary structure tensor direction is just a visualisation technique, which has limitations when it comes to assessing laminar organisation. Mapping 3D directions to RGB colours is inherently difficult and will always have ambiguities. If we had used the standard R-G-B to LR-AP-IS colouring in Figure 3, the laminar organisation would not be evident. Additionally, the laminae will only be visible when there are clear angular differences. There can still be a layered organisation even if we don’t observe it, which is the case for the mouse. The primary direction differences of these layers could be very low (i.e., parallel layers), and consequently not visually evident. This point has been clarified in both the Results and Discussion sections.

Finally, in response to R1.6, we have added analyses regarding the shape of the FOD, specifically estimating the Orientation Dispersion Index (ODI) and Dispersion Anisotropy (DA). This provides further context to the reviewer’s comments about the discrepancies in laminar organisation. We have reflected on the relationship between DA and the visually observed laminar organisation, and this has been integrated into the relevant parts of the Results and Discussion sections.

The changes to manuscript reflecting the statements above are listed here: 

The Discussion section (page 21): “In the monkey CC DESY data, which has a field of view (FOV) comparable to a dMRI voxel, a columnar laminar organisation at a macroscopic level was visually revealed from the structure tensor (ST) direction colouring. However, this laminar organisation was not visible in the higher-resolution ESRF data for the same tissue sample. Although the two samples were not co-registered, the size of a single ESRF FOV within the DESY sample is illustrated in Fig. 3A. This demonstrates the possibility of placing the ESRF sample where the observed laminar structure is absent. Consequently, knowledge of the tissue structural organisation and its orientation is important to fully benefit from the stacked FOV of the ESRF sample and when choosing appropriate minimal FOV sizes in future experiments.

Interestingly, when characterising FODs with measures like ODI and DA as indicators of fibre organisation, rather than relying on visualisation, results from large- and small-FOV data show no discrepancies. This statistical approach discards the spatial context (visually perceived as laminae), highlighting the need to combine both methods.” 

The Results section (page 8): “The mid-level DA values suggest some anisotropic spread of the directions, reflecting the angled laminar organisation observed in the DESY sample. Interestingly, the DA value for the ESRF sample is almost identical, despite the laminar bands being less visually apparent.”

The Results section (page 17): “Nevertheless, visualisation of orientations did not reveal any axonal organisation in the mouse CC due to the lack of local angular contrast, unlike the clear laminar structures seen in the monkey sample (Fig. 3A). Any parallel organisation in tissue remains undetectable because our visual contrast relies on angular differences.”

The Discussion section (page 22): “In the monkey CC (mid-body), we observed laminar organisation indicated by clear spatial angular differences in the ST directions in the sample (Fig. 3A). Quantifications of the FOD shape showed DA indices of 0.55 and 0.59 for the DESY and ESRF samples, respectively. In contrast, the mouse CC (splenium) did not visually reveal a similar angled laminar organisation (Fig. 7C), and the DA indices were lower, at 0.49 and 0.32, respectively. Two possible explanations exist. First, the within-pathway laminar organisation may not be identical across the entire CC. Consequently, more scans from other CC regions would be required to confirm. Second, the different species might account for the differences. Larger brains like the monkey might foster a different level of within-pathway axon organisation compared to the smaller mouse. Although we could not visually detect laminar organisation from the colour coding of the ST direction in the mouse, the non-zero DA values suggest some level of organisation. This is supported by our streamline tractography, which indicates a vertical layered organisation (Fig. 8A, B). It further aligns with studies using histological tracer mapping that shows a stacked parallel organisation of callosal projections in mice, between cortex regions M1 and S1 (Zhou et al. 2013). Nevertheless, we cannot rely solely on voxel-wise ST directions to fully describe axonal organisation, as this method does not contrast almost parallel fasciculi (inclination angles approaching 0 degrees). Analysing patterns in tractography streamlines would be an interesting future direction for this purpose.”

The authors further quantify various other characteristics of the white matter, such as micro-dispersion, tortuosity, and maximum displacement. Notably, the microscopic FA calculated via structure tensor is fairly consistent across regions, though not modalities. When fibre orientations are combined across the sample, they are shown to produce similar FODs to dMRI acquired in the same tissue, which is reassuring. As noted in the text, the estimates of tortuosity and max displacement are dependent on the FOV over which they are calculated. Calculating these metrics over the same FOV, or making them otherwise invariant to FOV, could facilitate more meaningful comparisons across samples and/or modalities.

R1.4: This raises an interesting point about the necessity of normalising the FOV to obtain invariant, tractography-based metrics of tortuosity and maximum deviation across different samples and modalities. In general, achieving this is challenging, and in this study, it is practically not possible. Between species, we encounter significant differences in brain volume ratios, which complicates the establishment of a common reference FOV due to the distinct anatomical organisation of monkey and mouse brains (see our response to R1.8). Within species, we would encounter challenges due to missing contrast—such as issues with staining—and the lack of perfect co-registration.

The Discussion section (page 28) has been extended to reflect this: ”Within the same species, assuming perfect co-registration of samples, it would be possible to perform correlative imaging and analysis. This would allow validation of whether tractography streamlines could be reproduced at different image resolutions within the same normalised FOV. Although this was not possible with the current data and experimental setup, it would be an interesting point to pursue in future work.”

Though the results seem solid, some statements, particularly in the abstract and discussion, do not seem to be fully supported by the data. For example, the abstract states "Our findings revealed common principles of fibre organisation in the two species; small axonal fasciculi and major bundles formed laminar structures with varying angles, according to the characteristics of major pathways.", though the results show "no strong indication within the mouse CC of the axonal laminar organisation observed in the monkey". Similarly, the introduction states: "By these means, we demonstrated a new organisational principle of white matter that persists across anatomical length scales and species, which governs the arrangement of axons and axonal fasciculi into sheet-like laminar structures." Further comments on the text are provided below.

R1.5: We understand that it can be misunderstood that the laminar organisation is identical in monkeys and mice, which is not the case. For example, we show that in the corpus callosum, pathways are parallel in the mouse but not in the monkey. We have clarified that while the principle of layered laminar organisation of pathways is shared between monkeys and mice, species-specific differences do exist.

We have made the following clarifying changes to the manuscript:

The Abstract (page 2): “Our findings revealed common principles of fibre organisation that apply despite the varying patterns observed across species” 

The Introduction (page 4-5): “Through these methods, we demonstrated organisational principles of white matter that persists across anatomical length scales and species. These principles govern the organisation of axonal fasciculi into sheet-like laminar shapes (structures with a predominant planar arrangement). Interestingly, while these principles remain consistent, they result in varied structural organisations in different species.” 

The Discussion (page 21): “despite species differences”.

One observation not notably discussed in the paper is that the spherical histograms of Figure 3E/H appear to have an anisotropic spread of the white points about 0,0. It would be interesting if the authors could comment on whether this could be interpreted as the FOD having asymmetric dispersion and if so, whether the axis of dispersion relates to the fibre orientations of the laminar structures.

R1.6: That is a good point, and to address it, we have fitted spherical Bingham distributions to the FODs, allowing us to quantify their shapes. From each Bingham distribution, we derived two wellknown indices from the diffusion MRI community: the Orientation Dispersion Index (ODI) and Dispersion Anisotropy (DA) index. The ODI explains the dispersion of fibres for a single bundle FOD, whereas DA expresses the shape of the FOD on the unit sphere surface, i.e., the degree of anisotropy. We have integrated the Bingham-based analysis into the Methods, Discussion, and Results sections concerning Figures 3 and 7, but not Figure 4, which contains multiple fibre bundles that we cannot separate on a voxel level. The analysis does not impact the overall message and conclusion but adds interesting context to the discussion around laminar organisation.

A limitation of the study is that it considers only small ex vivo tissue samples from two locations in a single postmortem monkey brain and slightly larger regions of mouse brain tissue. Consequently, further evidence from additional brain regions and subjects would be required to support more generalised statements about white matter organisation across the brain.

R1.7: Collecting more samples from various locations in the brain would provide valuable insights into the consistency of white matter organisation across anatomical length scales, as well as the structuretensor based anisotropy and tortuosity metrics. However, being awarded beamtime at two different synchrotron facilities to scan the same sample with different imaging setups is practically challenging. At the ESRF, we have gathered additional image volumes from other white matter regions of the monkey brain that support all our findings, which will be published separately. X-ray synchrotron imaging technology is advancing rapidly, with faster acquisition times enabling more image volumes to be stitched together. This extends the FOV and allows for a more robust statistical description of the anatomy. Consequently, future studies with an extended FOV and varying image resolutions could utilise a single synchrotron facility to collect additional samples, further supporting our findings.

The Discussion section (page 27) has been extended to reflect this: “Increasing the number of samples across both species and examining laminar organisation at various length scales in more regions would strengthen our findings. However, securing beamtime at two different synchrotron facilities to scan the same sample with varying image resolutions is a limiting factor. Beamline development for multiresolution experimental setups, along with faster acquisition methods, is a rapidly advancing field. For instance, the Hierarchical Phase-Contrast Tomography (HiP-CT) imaging beamline at ID-18 at the ESRF, enables multi-resolution imaging within a single session to address this challenge, though it is currently limited to a resolution of 2.5 μm (Walsh et al. 2021).”

Given the monkey results, the mouse study (section 2.5 onwards) lacks some motivation. In particular, it is unclear why a demyelination model was studied and if/how this would link to the laminar structure observed in the monkey data. Further, it is unclear how comparable tortuosity/max deviation values are across species, considering the differences in data quality and relative resolution, given that the presented results show these values are very modality-dependent.

R1.8: We have clarified the motivation for including the mouse part of the study in both the Introduction and the Results sections.

The Introduction section (page 5): “Furthermore, using a mouse model of focal demyelination induced by cuprizone (CPZ) treatment, we investigate the inflammation-related influence on axonal organisation. This is achieved through the same structure tensor-derived micro-anisotropy and tractography streamline metrics.”

The Results section (page 15): “Finally, we investigated the organisation of fasciculi in both healthy mouse brains and a murine model of focal demyelination induced by five weeks of cuprizone (CPZ) treatment. This allowed for the exploration of the disease-related influence on axonal organisation, particularly under inflammation-like conditions with high glial cell density at the demyelination site (He et al. 2021). The experimental setup for DESY and ESRF is similar to that described for the monkey, with the exception that we did not perform dMRI and synchrotron imaging on the same brains, and only collected MRI data for healthy mouse brains. This approach allowed us to apply the same structure tensor and tractography streamline analysis used previously, but in a healthy versus disease comparison, demonstrating the methodology’s ability to provide insights into pathological conditions.”

Across species, the comparison of tortuosity and maximum deviation must be approached with caution. On one hand, we observe a comparable influence of the extra-axonal environment in both the monkey and mice, as discussed in the section “Sources to the non-straight trajectories of axon fasciculi.” On the other hand, the anatomical scale and relative image resolution are significant factors, as correctly pointed out. In the mouse, for instance, the measures are influenced by white matter pathway macroscopic effects, making cross-species comparison challenging to perform in a normalised way.

The limitations section of the Discussion (page 28) has been updated to reflect this: ”A limiting consequence of having samples imaged at differing anatomical scales is that certain measures become inherently hard to compare in a normalised way. The tractography-based metrics—tortuosity and maximum deviation—serve as good examples of this resolution and FOV dependence. In the ESRF samples, the anatomical scale was at the level of individual axons, and the streamline metrics primarily reflect micro-scale effects from the extra-axonal environment, such as the influence of cells and blood vessels. In comparison, the larger anatomical scale in the DESY samples represents the level of fasciculi and above, with metrics influenced by macroscopic effects, such as the bending of the CC pathway. Both scales are interesting and can provide valuable insights in their own right, but caution is required when comparing the numbers, especially for cross-species studies where there is a significant difference in brain volume ratios.”

The paper introduces a new method of "scale-space" parameters for structure tensors. Since, to my understanding, this is the first description of the method, some simple validation of the method would be welcomed. Further, the same scale parameters are not used across monkeys and mice, with a larger kernel used in mice (Table 2) which is surprising given their smaller brain size. Some explanation would be helpful.

R1.9: We have expanded the description of the scale-space structure tensor approach in the Methods section. Specifically, we have elaborated on the empirical process used to select the scale-space parameters shown in Table 2 and explained why multiple scales were applied only to the monkey samples scanned at ESRF (see Table 2, sample IDs 2 and 3) but not to the other datasets. Additionally, we have added a supplementary figure to assist in illustrating the concept.

Reviewer #2 (Public Review):

Summary:

In this work, the authors combine diffusion MRI and high-resolution x-ray synchrotron phase-contrast imaging in monkey and mouse brains to investigate the 3D organization of brain white matter across different scales and species. The work is at the forefront of the anatomical investigation of the human connectome and aligns with several current efforts to bridge the resolution gap between what we can see in vivo at the millimeter scale and the complexity of the human brain at the sub-micron scale. The authors compare the 3D white matter organization across modalities within 2 small regions in one monkey brain (body of the corpus callosum, centrum semiovale) and within one region (splenium of the corpus callosum) in healthy mice and in one murine model of focal demyelination. The study compares measures of tissue anisotropy and fiber orientations across modalities, performs a qualitative comparison of fasciculi trajectories across brain regions and tissue conditions using streamlined tractography based on the structure tensor, and attempts to quantify the shape of fasciculi trajectories by measuring the tortuosity index and the maximum deviation for each reconstructed streamline. Results show measures of anisotropy and fiber orientations largely agree across modalities, especially for larger FOV data. The high-resolution data allows us to explore the fiber trajectories in relation to tissue complexity and pathology. The authors claim the study reveals new common organization principles of white matter fibers across species and scales, for which axonal fasciculi arrange into sheet-like laminar structures.

Strengths:

The aim of the study is of central importance within present efforts to bridge the gap between macroscopic structures observable in vivo in humans using conventional diffusion MRI and the microscopic organization of white matter tissue. Results obtained from this type of study are important to interpret data obtained in vivo, inform the development of novel methodologies, and expand our knowledge of the structural and thus functional organization of brain circuits.

Multi-scale data acquired across modalities within the same sample constitute extremely valuable data that is often hard to acquire and represent a precious resource for validation of both diffusion MRI tractography and microstructure methods.

The inclusion of multi-species data adds value to the study, allowing the exploration of common organization principles across species.

The addition of data from a murine cuprizone model of focal demyelination adds interesting opportunities to study the underlying biological changes that follow demyelination and how these impact tissue anisotropy and fiber trajectories. These data can inform the interpretation and development of diffusion MRI microstructure models.

Weaknesses:

The main claim of a newly discovered laminar organization principle that is consistent across scales and species is not supported strongly enough by the data. The main evidence in support of the claim comes from the larger FOV data obtained from the body of the corpus callosum in the monkey brain. A laminar organization principle is partially shown in the centrum semiovale in the monkey brain and it is not shown in mice data. Additionally, the methods lack details to help the correct interpretation of these findings (e.g., how were these fasciculi defined?; how well do they represent different axonal populations?; what is the effect of blood vessels on the structure tensor reconstruction?; how was laminar separation quantified?) and the discussion does not provide a biological background for this organization. The corpus callosum sample suggests axons within a bundle of fibers are organized in a sheet-like fashion, while data from the centrum semiovale suggest fibers belonging to different fiber bundles are organized in a sheet-like arrangement. While I acknowledge the challenges in acquiring such high-resolution data, additional samples from different regions in the same animals and from different animals would help strengthen this claim.

R2.1 

-  how were these fasciculi defined?

In the introduction (page 3), we have clarified our definition of an axon fasciculus: “A fasciculus is a bundle of axons that travel together over short or long distances. Its size and shape can vary depending on its internal organisation and its relationship to neighbouring fasciculi.”

Additionally, we emphasise in the Results section (page 12) that the centroid streamlines are not guaranteed to be actual fasciculi, but rather representations of them. The paragraph now states: “To ease visualisation and quantification, we used QuickBundle clustering(Garyfallidis et al. 2012) to group neighbouring streamlines with similar trajectories into a centroid streamline. This centroid streamline serves as an approximation of the actual trajectory of a fasciculus.”

- what is the effect of blood vessels on the structure tensor reconstruction?

Fair point, that was not clear from our description. The clarification contains two parts. First, the estimation of the structure tensor occurs in all voxels, and in that sense, the blood vessels respond very similarly to axons. Second, when it comes to sample statistics derived from the structure tensor analysis (FA histograms and the FODs), they will have an influence, albeit a small one, given the low volume percentage of the blood vessels within the FOVs. In the monkey samples, segmenting the blood vessels was achievable with little effort, allowing us to exclude their contribution from FA statistics and FODs. To make this clear, we have added a paragraph to the Methods section (page 34) titled “Structure tensor-based quantifications,” reflecting this clarification. Additionally, we have restructured the entire structure tensor methods description (starting on page 32) as part of the reviewer comments in R1.6 and R1.9.

- how was laminar separation quantified?

We have added a clarification in Results section (page 7): “The laminar thickness was determined by manual measurements on laminae visually identified in the 3D volume”.

- discussion does not provide a biological background for this organization.

A good point. Including the biological background is relevant as it supports the laminar organisation of white matter pathways observed in our findings and those of others.

We have added a section on this background in the Discussion (page 24): “We believe our observed topological rule of white matter laminar organisation can be explained by a biological principle known from studies of nervous tissue development. The first axons to reach their destination, guided by their growth cones, are known as “pioneering” axons. “Follower” axons use the shaft of the pioneering axon for guidance to efficiently reach the target region (Breau and Trembleau 2023). Axons can form a fasciculus by fasciculating or defasciculating along their trajectory through a zippering or unzipping mechanism, controlled by chemical, mechanical, and geometrical parameters. Zippering “glues” the axons together, while unzipping allows them to defasciculate at a low angle (Šmít et al. 2017). Although speculative, the zippering mechanism may be responsible for forming the laminar topology observed across length scales. The defasciculation effect can explain our results in the corpus callosum (CC) of monkeys, with laminar structures at low angles (~35 degrees) also observed by (Innocenti et al. 2019; Caminiti et al. 2009), as well as in other major pathways (Sarubbo et al. 2019). In contrast, a fasciculation mechanism may be observed in the mouse CC (0 degrees). If the geometrical angle between two axons is high, i.e., toward 90 degrees, the zippering mechanism will not occur, and the two axons (fasciculi) will cross (Šmít et al. 2017). This supports our and other findings that crossing fasciculi or pathways occur at high angles toward 90 degrees in the fully matured brain (Wedeen et al. 2012). Once myelination begins, the zippering mechanism is lost (Šmít et al. 2017), suggesting that laminar topology is established at the earliest stages of brain maturation.”

- additional samples from different regions in the same animals and from different animals would help strengthen this claim

Reviewer #1 also pointed to the inclusion of additional samples, and this is now discussed as part of the study limitations on page 27 (see also R1.7).

The main goal of the study is to bridge the organization of white matter across anatomical length scales and species. However, given the substantial difference in FOVs between the two imaging modalities used, and the absence of intermediate-resolution data, it remains difficult to effectively understand how these results can be used to inform conventional diffusion MRI. In this sense, the introduction does not do a good enough job of building a strong motivation for the scientific questions the authors are trying to answer with these experiments and for the specific methodology used.

R2.2: Indeed, this is an essential point now emphasised in the introduction, page 3, which now states: ”Despite the limited resolution of dMRI, the water diffusion process can reveal microstructural geometrical features, such as axons and cell bodies, though these features are compounded at the voxel level. Consequently, estimating microstructural characteristics depends on biophysical modelling assumptions, which can often be simplistic due to limited knowledge of the 3D morphology of cells and axons and their intermediate-level topological organisation within a voxel. Thus, complementary highresolution imaging techniques that directly capture axon morphology and fasciculi organisation in 3D across different length scales within an MRI voxel are essential for understanding anatomy and improving the accuracy of dMRI-based models(Alexander et al. 2019).”

Additionally, in the introduction, page 4, we have made the following changes to strengthen the link across modalities, such that it now states: “In the x-ray synchrotron data, we applied a scale-space structure tensor analysis, which allowed for the quantification of structure tensor-derived tissue anisotropy and FOD in the same anatomical regime indirectly detected by dMRI.”

The cuprizone data represent a unique opportunity to explore the effect of demyelination on white matter tissue. However, this specific part of the study is not well motivated in the introduction and seems to represent a missed opportunity for further exploration of the qualitative and quantitative relationship between diffusion MRI and sub-micron tissue information (although unfortunately not within the same brain sample). This is especially true considering the diffusion MRI protocol for mice would allow extrapolation of advanced measures from different tissue compartments.

R2.3: A similar point was raised by Reviewer 1 (R1.8), and we have clarified the motivation for including the healthy mice and the demyelination samples.  

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Many thanks to the authors for providing open data. This was very helpful when reviewing the manuscript and is a valuable resource for the community.

R1.10: We are happy to share our data with the community. Understanding anatomy in 3D is hard to achieve through still images and animations, so the ability to explore it on your own is quite important. The link to the data repository has been added in the Methods section in the following paragraph: “Due to the size of the data selected, processed image volumes, masks and results are available at https://zenodo.org/records/10458911. Other datasets can be shared on request.“

One confusing element of the paper is that orientations (or axes) do not seem to be consistent across samples/modalities. For example, the green tensors in Figures 3 C and D are tilted up/down in opposite directions and the streamlines in Figure 5A seem opposite (SL) from what we would expect from Figure 2A (SR). Having consistent orientations across modalities and images would help the reader. When colouring tensors (e.g. in Figure 3), the authors could consider a 3D colour scheme (similar to that used by diffusion MRI) rather than colouring by only inclination, as this would provide useful information on whether different laminae have similar orientations, as implied by the tractography in Figure 4.

R1.11: Thank you for spotting the suboptimal consistency between Figures 2, 3, and 5. Figure 2 has been corrected and updated. The left-right direction in the coronal views was not correctly displayed. Additionally, the glyph directions have been updated in Figures 2 and 3.

By default, we use the “standard” RGB colour scheme used in dMRI. However, for the monkey CC— essentially Figure 3—this did not effectively illustrate our findings. We decided to use a different directional colour encoding scheme, which captures the angular deviation from the L-R axis. This was to assist in the visualisation of the inclination angle between the laminars. We have used the same colour scheme for the tensors in Figure 3 to avoid confusion.

On a general note, the standard colour scheme has uniform “colour contrast” in all directions, but when there is only a single dominant direction in the sample, it can make sense to concentrate the colour contrast in that axis.

Results: "even higher FA anisotropy in the micro-tensor domain of 0.997, i.e., the micro (μ)FA (20, 21)." I understand these references lead to a definition of μFA that is based on multiple diffusion tensor encodings which is quite different from that suggested by Kaden. It may be preferable to reference Kaden directly (since I understand this is the method used) to avoid confusion.

R1.12: Correctly spotted, and we now reference the method from Kaden et al. and use the other references elsewhere when relevant.

"and scanned the mouse brain in a whole." - typo?

R1.13: Thank you for spotting the typo. The mouse brain was kept in the skull during MRI scanning, which has been clarified in the Methods section.

The crossing fibre region appears to be sometimes referred to as the centrum semiovale, and other times as the CST. CS seems the better description and keeping this naming consistent would avoid confusion to the reader.

R1.14: Well spotted, thank you. We have replaced the usage of Corticospinal Tract (CST) with centrum semiovale (CS) where relevant.

Direct comments on the text:

Abstract: "Individual axon fasciculi exhibited tortuous paths .... in a manner independent of fibre complexity and demyelination"

Do statistical comparisons of the various distributions support this? The data shows somewhat increased tortuosity in the CST compared to the CC, and somewhat lower tortuosity in CPZ tissue.

R1.15: The intention of the text was not to point to the comparison of tortuosity, but rather to highlight the maximum deviation. We observe a high probability density of maximum deviations at approximately 5-10 microns in all samples, which corresponds to the size of structures in the extraaxonal environment, such as blood vessels and cells.

Additionally, we understand that the original statement might imply an expectation of a statistical analysis demonstrating independence, which is not the case. To clarify, we have reformulated the sentence in the Abstract (page 2) to address these points: “Fasciculi exhibited non-straight paths around obstacles like blood vessels, comparable across the samples of varying fibre complexity and demyelination.”

Abstract: "A quantitative analysis of tissue anisotropies and fibre orientation distributions gave consistent results for different anatomical length scales and modalities, while being dependent on the field-of-view."

To my understanding, the FODs here from different modalities are calculated over different FOVs (in monkeys at least), and FODs are only presented for a single FOV for each modality, meaning it is difficult to separate the effects of modality from FOV. The microscopic anisotropy is also noticeably different across modalities (DESY < ESRF < dMRI).

R1.16: That is a fair point. Our statement was trying to capture too much condensed content to be correctly interpretable. We have reformulated the sentence to state: “Quantifications of fibre orientation distributions were consistent across anatomical length scales and modalities, whereas tissue anisotropy had a more complex relationship, both dependent on the field-of-view”.

While it is true that we only present the ST-derived quantifications – FOD and FA statistics – for a single FOV per modality and sample, the results shown for the ESRF monkey samples (Figures 3 and 4) are a merge of four individually processed volumes. The quantifications of each individual subFOV have now been added as a supplementary figure (Figure S3) to highlight the consistency of the methodology and the effect of shifting the FOV position. In the case of the mouse, we have two volumes from different mice, which also display similar FOD and FA statistics.

Abstract: "Our study emphasises the need to balance field-of-view and voxel size when characterising white matter features across anatomical length scales."

This point does not seem very well explored in the paper, rather it is an observation of the limitations of the different imaging modalities. For example, there aren't analyses to compare metrics from highresolution data at different FOVs (i.e. by taking neighbourhoods of different sizes), nor are metrics compared from data at different resolutions and the same FOV.

R1.17: The question is related to R1.16, R1.4, and R1.8, and we have addressed this point in our responses to those comments.

Figure 7 - Taking into account the eigenvalues can be helpful when interpreting the secondary and tertiary eigenvectors of tensors (V2 and V3). It would be interesting to know whether the eigenvalues L2 ~= L3 are approximately equal (suggesting isotropic diffusion about V1, where the definition of V2 versus V3 isn't very meaningful), or if L2 is noticeably larger than L3 (suggesting anisotropic diffusion about V1, potentially similar to the anisotropic dispersion discussed above).

R1.18: It would be interesting to explore the eigenvalues of the structure tensor in more detail, as has been done for the diffusion tensor. However, we believe this belongs to future work, as such additional detailed methodological analysis would complicate the already complex story. As mentioned in response to R1.10, most processed data has been made publicly available, and the rest can be requested (due to the storage size of the data sets) to perform such additional analysis.

Discussion: "Importantly, our findings revealed common principles of fibre organisation in both monkeys and mice; small axonal fasciculi and major bundles formed sheet-like laminar structures," See above regarding the lack of evidence for laminar structures in mouse data.

R1.19: We have reformulated the text for clarification as part of R1.3. Additionally, we added FOD quantifications to support why we do not observe an apparent laminar organisation in the mouse CC— please see our response to R1.6.

Discussion: "Interestingly, the dispersion magnitude is indicative of fasciculi that skirt around obstacles in the white matter such as cells and blood vessels, and the results are largely independent of both white matter complexity (straight vs crossing fibre region) and pathology." Again, do statistical tests of the various distributions support this?

R1.20: As part of R1.1, we have added statistical tests of significance for the quantifications of how max deviation changes when bending around objects. Indeed, the distributions are not statistically the same, and we do not wish to convey that sentiment, but they are comparable in the object sizes that they detect. As done in the abstract, we have reformulated the sentence to avoid misunderstanding and have replaced “largely independent” with “observed across.”

Discussion: "Tax et al. have demonstrated the calculation of a sheet probability index from diffusion MRI data, which suggested the presence of sheet-like features in the CC"

My understanding was that this was observed in crossing fibre regions, such as where fibres projecting with the CC cross the CST, but not the main body of the CC itself. Tax defines sheet structure as "composed of two tracts that cross each other on the same surface in certain regions along their trajectories." Is this a different phenomenon to the laminar structures observed here (where we observe fibres within a single tract being locally organised into laminar structures)?

R1.21: Thank you for pointing our attention to this. We have corrected the section in the Discussion (page 23), so it now states: “Additionally, Tax et al. have demonstrated the calculation of a gridcrossing sheet probability index from diffusion MRI data, which suggested the presence of sheet-like features in a crossing fibre region (Tax et al. 2016), which is in line with our findings in the synchrotron data. Note that the method by Tax et al. only detects sheet-like structures crossing on a grid and does not reveal laminar structures with lower inclination angles, as we observed in the monkey CC.”

Discussion: "We found that FODs were consistent across image resolutions and modalities, but only given that the FOV is the same." See above.

R1.22: As part of our response to R1.6, we quantified the FODs using the ODI and DA indices, which should help support our statement. Nevertheless, we have toned down the statement and reformulated the text as follows: “We found that FODs were comparable across image resolutions and modalities. The observed discrepancies can be attributed to the fact that the FOVs are not exactly matched.”

Discussion: "microscopic FA were highly correlated across modalities."

The data shows FA is considerably lower in DESY to ESRF; within modality FA is quite consistent irrespective of tissue region; and differences between the CC and CG shown in ESRF data in mice are not repeated in DESY. It is unclear from the current data if this would lead to a high correlation across modalities. Some evidence would be helpful.

R1.23: This is a fair point; we have not performed a correlation analysis. However, the pattern we observe for the synchrotron samples is as follows: When the anatomical length scale increases (becomes more macroscopic), the FA distribution shifts to lower values. This reflects the scale of information captured with the ST analysis (see also R1.9). Therefore, the most interesting comparison of FA statistics occurs when the resolution and anatomical length scale are approximately the same.  The sentence in question has been reformulated to the following: ”Estimates of structure tensor derived microscopic FA show a clear pattern across modalities.”

Discussion: "If so, the (inclination angle) information might serve to form rules for low-resolution diffusion MRI based tractography about how best to project through bottleneck regions, which is currently a source of false-positives trajectories (6)."

This is an interesting idea but it is unclear to me how this inclination information would help track through bottlenecks where, by definition, fibres are passing through with the same orientation. Some further explanation would be helpful.

R1.24: We have elaborated on the section in the Discussion (page 23), explaining how this can be used to improve tractography tracing through complex regions: “The reason is that standard tractography methods do not "remember" or follow anatomical organisation rules as they trace through complex regions. Our findings on pathway lamination and inclination angles—low for parallel-like trajectories and high for crossing-like trajectories—can help incorporate trajectory memory into these methods, reducing the risk of false trajectories”.

Reviewer #2 (Recommendations For The Authors):

Below I report comments that if addressed I believe would improve the clarity and readability of the manuscript.

-  Figures 1 and 2 would be more meaningful if combined into one figure. This would allow for a direct visual comparison of the two modalities. If space is needed, I believe the second row of Figure 1 (coronal views of CC) does not add much information. It is often hard to navigate the different orientations of the tissue in the images; thus any effort in trying to help the reader visually clarify would improve readability.

R2.4: We considered the reviewer’s suggestion to merge Figures 2 and 3. However, this made both the figures and the main text additionally complex, so we chose to retain the original figure layout. Secondly, Figure 3 utilises a non-standard directional colormap. Keeping the colormap consistent within each figure is a feature we wish to preserve. In response to R1.11, the figures have been updated to have more consistent orientations for the monkey samples.

In Figure 2, the second row, showing a coronal view of the CC, is essential for comparison with human data in Figure S1. It highlights where we observed the columnar laminar organisation and their inclination angle, as also detected by DTI.

-  Figure 4 shows synchrotron data revealing an anterior-posterior component within the centrum semiovale that is not necessarily seen in the dMRI data. Could the authors comment on this?

R2.5: Thank you for pointing this out. We have now addressed this in the Results section (page 10), where we describe the observation in detail: “Interestingly, visual inspection of the colour-coded structure tensor directions in Fig. 4E shows the existence of voxels whose primary direction is along the A-P axis. However, this represents a small enough portion of the volume that it does not appear as a distinct peak on the FOD.“

-  The authors claim they observed several purple axons crossing orthogonally in Figure 5c. However, that is not necessarily clear in the figure.

R2.6: We appreciate the feedback. We have now coloured the streamlines of the crossing fasciculi in Figure 5C in red.

-  Figure 5 would benefit from adding the color encoding scheme for Figure 5d, as sometimes this is not necessarily consistent.

R2.7: We appreciate the feedback. We have added an indication of the standard directional colour coding to Figure 5D.

-  Figure 5d shows interesting data from the complex region. However, it is hard to visualize and it looks like there are not many streamlines traveling entirely I-S? Maybe a different orientation of the sample would help visualization.

R2.8: A similar point was raised by Reviewer 1 (see R1.2). We have added an animation of the scene to assist in the interpretation of the 3D organisation within this complex sample.

-  The concept of axon fasciculi is not necessarily immediately clear. Adding an explanation for what the authors refer to when using this term would improve clarity.

R2.9: In the introduction, we now state our conceptual definition of an axon fasciculus as a number of axons that follow each other (see also R2.1).

-  The methods do not provide details on how structure tensor FA is measured.

R2.10: Thank you for pointing this out. We have restructured and expanded the structure tensor description in the Methods section (see also R1.9 and R2.1), which now includes the definition of FA.

-  Why didn't the authors select the same cc region for both mice and monkeys? It seems this would have increased the strength of the comparison.

R2.11: We agree. The reason lies in the chronology of experiments and the fact that we cannot control where demyelination takes place. We have added a clarifying description in the Methods section (page 31): “Note that several separate beamline experiments were conducted to collect the volumes listed in Table 1. In the first two experiments, samples from the monkey brain were scanned at ESRF and DESY, respectively. The samples from the mouse brain were imaged in two subsequent experiments. Consequently, the location of the identified demyelinating lesion in the cuprizone mice, which cannot be precisely controlled, did not match the location of the CC biopsies in the monkey.”

-  While it is mentioned in the results, the methods do not explain how vessel segmentations or cell segmentation in mice was performed and for which datasets it was performed.

R2.12: For the small ROI shown in Figure 6, the labelling was a manual process using the software ITK-SNAP, which has now been clarified in the corresponding figure caption. The generation of ROI masks and blood vessel segmentations involved a combination of intensity thresholding, morphological operations, and manual labelling in ITK-SNAP. This has been clarified in the restructured and expanded description of structure tensor analysis in the Methods section (starting on page 32).

-  From the methods it is hard to understand (1) how many mice were used; (2) why dMRI was done on a different sample; (3) whether the same selenium region was selected for both healthy and CPZ animals; (4) how the registration across samples was performed.

R2.13: We appreciate the feedback and have inserted clarifying statements in the relevant parts of the Methods section. (1) The total number of mice included was three: one normal, one cuprizone, and one normal for MRI scanning. (2) The quality of the collected dMRI on the mouse was too poor to use, and it could not be redone as the brain had already been sliced and prepared for synchrotron experiments. (3) The same splenium section was selected for both healthy and cuprizone mice. (4) A paragraph on image registration has been added.

-  Diffusion MRI method sections would benefit from additional details on the protocols used.

R2.14: Thank you for pointing this out. We have added more details about the diffusion MRI protocols, including the b-value, gradient strength, and other relevant parameters.

eLife Assessment

This valuable study extends the previous interesting work of this group to address the potentially different control of movement and posture. Through experiments in which stroke participants used a robotic manipulandum, the authors provide solid evidence supporting a lack of a relation between the resting force postural bias they measure (closely related to the flexor synergy in stroke) and kinematic deficits during movement. Based on these results, the authors propose a conceptual framework that differentially weights the two main descending pathways (corticospinal tract and reticulospinal tract) for neurologically intact and stroke patients. Discussing the potential impact of differences on muscle/spinal circuit state and their responses between holding a posture and movement, as well as the assumptions of their statistical comparisons, would further improve the paper.

Reviewer #1 (Public review):

This study extends the previous interesting work of this group to address the potentially differential control of movement and posture. Their earlier work explored a broad range of data to make the case for a downstream neural integrator hypothesized to convert descending velocity movement commands into postural holding commands. Included in that data were observations from people with hemiparesis due to stroke. The current study uses similar data, but pushes into a different, but closely related direction, suggesting that these data may address the independence of these two fundamental components of motor control. I find the logic laid out in the second sentence of the abstract ("The paretic arm after stroke is notable for abnormalities both at rest and during movement, thus it provides an opportunity to address the relationships between control of reaching, stopping, and stabilizing") less then compelling, but the study does make some interesting observations. Foremost among them, is the relation between the resting force postural bias and the effect of force perturbations during the target hold periods, but not during movement. While this interesting observation is consistent with the central mechanism the authors suggest, it seems hard to me to rule out other mechanisms, including peripheral ones. These limitations should should be discussed.

Reviewer #2 (Public review):

Summary:

Here the authors address the idea that postural and movement control are differentially impacted with stroke. Specifically, they examined whether resting postural forces influenced several metrics of sensorimotor control (e.g., initial reach angle, maximum lateral hand deviation following a perturbation, etc.) during movement or posture. The authors found that resting postural forces influenced control only following the posture perturbation for the paretic arm of stroke patients, but not during movement. They also found that resting postural forces were greater when the arm was unsupported, which correlated with abnormal synergies (as assessed by the Fugl-Meyer). The authors suggest that these findings can be explained by the idea that the neural circuitry associated with posture is relatively more impacted by stroke than the neural circuitry associated with movement. They also propose a conceptual model that differentially weights the reticulospinal tract (RST) and corticospinal tract (CST) to explain greater relative impairments with posture control relative to movement control, due to abnormal synergies, in those with stroke.

Comments on revisions:

The authors should be commended for being very responsive to comments and providing several further requested analyses, which have improved the paper. However, there is still some outstanding issues that make it difficult to fully support the provided interpretation.

The authors say within the response, "We would also like to stress that these perturbations were not designed so that responses are directly compared to each other ***(though of course there is an *indirect* comparison in the sense that we show influence of biases in one type of perturbation but not the other)***." They then state in the first paragraph of the discussion that "Remarkably, these resting postural force biases did not seem to have a detectable effect upon any component of active reaching but only emerged during the control of holding still after the movement ended. The results suggest a dissociation between the control of movement and posture." The main issue here is relying on indirect comparisons (i.e., significant in one situation but not the other), instead of relying on direct comparisons. Using well-known example, just because one group / condition might display a significant linear relationship (i.e., slope_1 > 0) and another group / condition does not (slope_2 = 0), does not necessarily mean that the two groups / conditions are statistically different from one another [see Figure 1 in Makin, T. R., & Orban de Xivry, J. J. (2019). Ten common statistical mistakes to watch out for when writing or reviewing a manuscript. eLife, 8, e48175.].

The authors have provided reasonable rationale of why they chose certain perturbation waveforms for different. Yet it still holds that these different waveforms would likely yield very different muscular responses making it difficult to interpret the results and this remains a limitation. From the paper it is unknown how these different perturbations would differentially influence a variety of classic neuromuscular responses, including short-range stiffness and stretch reflexes, which would be at play here.

Much of the results can be interpreted when one considers classic neuromuscular physiology. In Experiment 1, differences in resting postural bias in supported versus unsupported conditions can readily be explained since there is greater muscle activity in the unsupported condition that leads to greater muscle stiffness to resist mechanical perturbations (Rack, P. M., & Westbury, D. R. (1974). The short-range stiffness of active mammalian muscle and its effect on mechanical properties. The Journal of physiology, 240(2), 331-350.). Likewise muscle stiffness would scale with changes in muscle contraction with synergies. Importantly for experiment 2, muscle stiffness is reduced during movement (Rack and Westbury, 1974) which may explain why resting postural biases do not seem to be impacting movement. Likewise, muscle spindle activity is shown to scale with extrafusal muscle fiber activity and forces acting through the tendon (Blum, K. P., Campbell, K. S., Horslen, B. C., Nardelli, P., Housley, S. N., Cope, T. C., & Ting, L. H. (2020). Diverse and complex muscle spindle afferent firing properties emerge from multiscale muscle mechanics. eLife, 9, e55177.). The concern here is that the authors have not sufficiently considered muscle neurophysiology, how that might relate to their findings, and how that might impact their interpretation. Given the differences in perturbations and muscle states at different phases, the concern is that it is not possible to disentangle whether the results are due to classic neurophysiology, the hypothesis they propose, or both. Can the authors please comment.

The authors should provide a limitations paragraph. They should address 1) how they used different perturbation force profiles, 2) the muscles were in different states which would change neuromuscular responses between trial phase / condition, 3) discuss a lack of direct statistical comparisons that support their hypothesis, and 4) provide a couple of paragraphs on classic neurophysiology, such as muscle stiffness and stretch reflexes, and how these various factors could influence the findings (i.e., whether they can disentangle whether the reported results are due to classic neurophysiology, the hypothesis they propose, or both).

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public Review):

This study extends the previous interesting work of this group to address the potentially differential control of movement and posture. Their earlier work explored a broad range of data to make the case for a downstream neural integrator hypothesized to convert descending velocity movement commands into postural holding commands. Included in that data were observations from people with hemiparesis due to stroke. The current study uses similar data but pushes into a different, but closely related direction, suggesting that these data may address the independence of these two fundamental components of motor control. I find the logic laid out in the second sentence of the abstract ("The paretic arm after stroke is notable for abnormalities both at rest and during movement, thus it provides an opportunity to address the relationships between control of reaching, stopping, and stabilizing") less than compelling, but the study does make some interesting observations. Foremost among them, is the relation between the resting force postural bias and the effect of force perturbations during the target hold periods, but not during movement. While this interesting observation is consistent with the central mechanism the authors suggest, it seems hard to me to rule out other mechanisms, including peripheral ones. 

Response 1.1. Thank you for your comments, which we address in detail below and in our response to Recommendations to the authors (see pp. 15-19 of this letter). We would first like to clarify the motivation behind our use of a stroke population to understand the interactions between the control of reaching in and holding. We agree that this idea can be laid out in a more compelling way.

The fact that stroke patients usually display issues with their control of both reaching and holding, allows for within-individual comparisons of those two modes of control. Further, the magnitude of abnormalities is relatively large, making it easier to measure, compare and investigate effects. And, importantly, these two modes of control can be differentially affected after stroke (also pointed out by Reviewer 2, point 4 in Comments to the Authors). Finally, this kind of work – examining interactions between positive signs of stroke (such as abnormal posture or synergy) vs. negative signs (such as loss of motor control) – needs to be done in humans, as positive signs are relatively absent even in primates (Tower, 1940).

We have changed our abstract (changes shown below in red), and our intro (expanding the second paragraph, lines 75-76), to lay out our motivation more clearly.

From the abstract:

“The paretic arm after stroke exhibits different abnormalities during rest vs. movement, providing an opportunity to ask whether control of these behaviors is independently affected in stroke. “

On the other hand, the relation between force bias and the well-recognized flexor synergy seems rather self-evident, and I don't see that these results add much to that story.

Response 1.2. While it seems natural that these biases would be the resting expression of abnormal flexor synergies (given their directionality towards the body, as shown in Figures 2-3, and the other similarities we demonstrate in Figure 8), we do not believe it is self-evident. These biases are measured at rest, with the patient passively moved and held still, whereas abnormal synergies emerge when the patient actively tries to move. The lack of relationship we find between these resting force biases and active movement underlines that the relation between force bias and flexor synergy should not be taken as self-evident, making it worthwhile to examine it (as we motivate in lines 589-596 and show in Figure 8).

The paradox here is that, in spite of a relationship between force bias and flexor synergy (itself manifesting during attempted movement), there seems to be no relationship between force bias and direct measures of active movement (Figures 5,6). This is the paradox that inspired our conceptual model (Figure 9) and inspires to further investigate the factors under which these two systems are intermingled or kept separate. We thus find it to be a helpful element in the story.

I am also struck by what seems to be a contradiction between the conclusions of the current and former studies: "These findings in stroke suggest that moving and holding still are functionally separable modes of control" and "the commands that hold the arm and finger at a target location depend on the mathematical integration of the commands that moved the limb to that location." The former study is mentioned here only in passing, in a single phrase in the discussion, with no consideration of the relation between the two studies. This is odd and should be addressed. 

Response 1.3. While these two sets of findings are not contradictory, we understand how they can appear as such without providing context. We now discuss the relationship between our present study and the previous one more directly (lines 66-70 and 663-669 of the revised manuscript).

The previous study examined how the control of movement informs the control of holding after the movement was over; the current study examines whether abnormalities in holding measured at rest with the movement leading to the rest position being passive. There are thus two important distinctions:

First, directionality of potential effects: here we examine the effect of (abnormalities in) holding control upon movement, but the 2020 study (Albert et al., 2020) examines the effects of movement upon holding control. Stroke patient data in the 2020 study showed that, under CST damage, while the reach controller is disrupted, the hold controller can continue to integrate the malformed reach commands faithfully. In line with this, we proposed a model where the postural controller system sits downstream of the moving controller (Figure 7G in the 2020 paper). We thus did not claim, in 2020, that integration of movement commands is the only way to do determine posture control, as we stated explicitly back then, e.g. (emphasis ours):

“Equations (1) and (2) describe how the integration of move activity may relate to changes in hold commands, but does not specify the hold command at the target.”

In short, finding no effect of holding abnormalities upon movement (present finding) does not mean there is no potential effect of movement upon holding (2020 finding). This is something we had alluded to in the Discussion but not clarified, which we do now (see edits at the end of our response to this point).

Second, active vs. passive movement: here, we measure holding control at rest (Experiment 1). The 2020 study shows that endpoint forces reflect the integration of learned dynamics exerted during active movement that led to the endpoint position. However, in Experiment 1, there is no active reaching to integrate, as the robot passively moves the arm to the held position. Thus, resting postural forces measured in Experiment 1 could not reflect the integration of reach commands that led to each rest position.  

Thus, the two sets of findings are not contradictory. Taking our current and 2020 findings together suggests that active holding control would comprise would reflect both the integration of movement control that led to assuming the held position, plus the force biases measured at rest.

Hence our decision to describe these two systems as functionally separable: while these systems can interact, the effects of post-stroke malfunctions in each can be independent depending on the function and conditions at hand. This does not make this a limited finding: being able to dissociate post-stroke impairment based on each of these two modes of control may inform rehabilitation, and also importantly, understanding the conditions in which these two modes of control become separable can substantially advance our understanding of both how different stroke signs interact with each other and how motor control is assembled in the healthy motor system. Figure 9 illustrates our conceptual model behind this and may serve as a blueprint to further dissect these circuits in the future.

We discuss these issues briefly in lines 663-669 in our Discussion section, reproduced below for convenience:

“It should be noted, however, that having distinct neural circuits for reaching and holding does not rule out interactions between them. For example, we recently demonstrated how arm holding control reflects the integration of motor commands driving the preceding active movement that led to the hold position, in both healthy participants and patients with hemiparesis (Albert et al., 2020). However, in that paper, we did not claim that this integration is the only source of holding control. Indeed, in Experiment 1 of the current study, we used passive movement to bring the arm to each probed position, which means that the postural biases could not be the result of integration of motor commands.” 

And, we have adjusted our Introduction to provide pertinent context regarding our 2020 work (first paragraph, lines 66-70 of the updated manuscript).

A minor wording concern I had is that the term "holding still" is frequently hard to parse. A couple of examples: "These findings in stroke suggest that moving and holding still are functionally separable modes of control." This example is easily read, "moving and holding [continue to be] functionally separable". Another: "...active reaching and holding still in the same workspace, " could be "...active reaching and holding [are] still in the same workspace." Simply "holding", "posture" or "posture maintenance" would all be better options.

Response 1.4. Thank you for your suggestion. Following your comment, we have abbreviated this term to simply “holding”, both on the title and throughout the text.

Reviewer #2 (Public Review):

Summary: 

Here the authors address the idea that postural and movement control are differentially impacted with stroke. Specifically, they examined whether resting postural forces influenced several metrics of sensorimotor control (e.g., initial reach angle, maximum lateral hand deviation following a perturbation, etc.) during movement or posture. The authors found that resting postural forces influenced control only following the posture perturbation for the paretic arm of stroke patients, but not during movement. They also found that resting postural forces were greater when the arm was unsupported, which correlated with abnormal synergies (as assessed by the Fugl-Meyer). The authors suggest that these findings can be explained by the idea that the neural circuitry associated with posture is relatively more impacted by stroke than the neural circuitry associated with movement. They also propose a conceptual model that differentially weights the reticulospinal tract (RST) and corticospinal tract (CST) to explain greater relative impairments with posture control relative to movement control, due to abnormal synergies, in those with stroke.

Strengths: 

The strength of the paper is that they clearly demonstrate with the posture task (i.e., active holding against a load) that the resting postural forces influence subsequent control (i.e., the path to stabilize, time to stabilize, max. deviation) following a sudden perturbation (i.e., suddenly removal of the load). Further, they can explain their findings with a conceptual model, which is depicted in Figure 9. 

Weaknesses: 

Current weaknesses and potential concerns relate to i) not displaying or reporting the results of healthy controls and non-paretic arm in Experiment 2 and ii) large differences in force perturbation waveforms between movement (sudden onset) and posture (sudden release), which could potentially influence the results and or interpretation. 

Response 2.0. Thank you for your assessment, and for pointing out ways to improve our paper. We address the weakness and potential concerns in detail below.

Larger concerns

(1) Additional analyses to further support the interpretation. In Experiment 1 the authors present the results for the paretic arm, non-paretic arm, and controls. However, in Experiment 2 for several key analyses, they only report summary statistics for the paretic arm (Figure 5D-I; Figure 6D-E; Figure 7F). It is understood that the controls have much smaller resting postural force biases, but they are still present (Figure 3B). It would strengthen the position of the paper to show that controls and the non-paretic arm are not influenced by resting postural force biases during movement and particularly during posture, while acknowledging the caveat that the resting positional forces are smaller in these groups. It is recommended that the authors report and display the results shown in Figure 5D-I; Figure 6D-E; Figure 7F for the controls and non-paretic arm. If these results are all null, the authors could alternatively place these results in an additional supplementary. 

Response 2.1a. Thank you for your recommendations. We agree both on the value of these analyses and the caveat associated with them: these resting postural force biases are substantially smaller for the non-paretic and control data (for example, the magnitude of resting biases in the supported condition is 2.8±0.4N for the paretic data, but only 1.8±0.4N and 1.3±0.2N for the non-paretic and control data, respectively; the difference is even greater in the unsupported condition, though this is not the one being compared to Experiment 2).

We now conduct a comprehensive series of supplementary analyses, including the examination of non-paretic and control data for all three components of Experiment 2 (unperturbed reaches; pulse perturbations; and active holding control). These are mentioned in the Results (lines 422-424, 512513, and 574-574 of the revised manuscript) and illustrated in the supplementary materials: Supplementary Figures S5-1, S6-1, and S7-1 contain the main analyses (comparisons of instances with the most extreme resting biases for each individual) for the unperturbed reach analysis, pulse perturbation analysis, and active holding control analysis, respectively.

We find that non-paretic and control data do not display effects of resting biases upon unperturbed reaching control (Figure S5-1) or control against a pulse perturbation early during movement (Figure S6-1) – as is the case with the paretic data. Non-paretic and control data do not display evidence of influence of their resting force biases upon active holding control either (Figure S7-1), unlike the paretic data. For the non-paretic data, however, these influences are nominally towards the same direction as in the paretic data. Given that resting biases are substantially weaker for the non-paretic case, it is possible a similar relationship exists but requires increased statistical power to discern. Moreover, it is possible that the effect of resting biases is non-linear, with small biases effectively kept under check so that their impact upon active holding control is even less than a linearly scaled version of the impact of the stronger, paretic-side biases. This can be the subject of future work.

Please also note that, following your recommendation (Recommendations to the Authors, point 2.1), we have conducted secondary analyses which estimate sensitivity to resting bias using all datapoints, validating our main analyses; these analyses were also performed for control and non-paretic data, with similar results (Response 2.A.1).

Further, the results could be further boosted by reporting/displaying additional analyses. In Figure 6D the authors performed a correlation analysis. Can they also display the same analysis for initial deviation and endpoint deviation for the data shown in Figure 5D-F & 5G-I, as well for 7F for the path to stabilization, time to stabilization, and max deviation? This will also create consistency in the analyses performed for each dependent variable across the paper.

Response 2.1b. Here, we set to test whether resting biases affect movement. It is best to do this using a within-individual comparison design, rather than using across-individual correlations: while correlation analyses can in general be informative, they obscure within-individual effects which are the main comparisons of interest in our study. Consider a participant with strong resting bias towards one direction, tested on opposing perturbations; averaging these responses for each individual would mostly cancel out any effects of resting biases. Even if we were to align responses to the direction of the perturbation before averaging, the power of correlation analyses may be diluted by inter-individual differences in other factors, such as overall stiffness.

Thus, our analysis design was instead focused on examining the differential effects of resting posture biases within each individual’s data. We compared the most extreme opposing/aligned or clockwise/counter-clockwise instances within each individual, specifically to assess these differential effects. In our revised version, we have further reinforced these analyses to include all data rather than the most extreme instances (see response 2.A.1.a to the Reviewer’s recommendation to the authors) where we performed correlations of within-individual resting posture vs. the corresponding dependent variables and compared the resulting slopes. 

The across-individual correlation analyses add little to that for the reasons we outlined above. At the same time, it is possible they can be helpful in e.g. illustrating across-individual variability. We thus now include across-individual correlation analyses for all dependent variables, but, given their limited value, only in the supplementary material. This also means that, for consistency, we moved the correlation analysis in Figure 6 to the corresponding supplementary figure as well (Figure S6-3).

In addition, following the Reviewer’s comment about consistency in the analyses performed for each dependent variable across the paper, we added within-individual comparisons for settling time following the pulse perturbations (Figure 6D, right).

(2) Inconsistency in perturbations that would differentially impact muscle and limb states during movement and posture. It is well known that differences in muscle state (activation / preloaded, muscle fiber length and velocity) and limb state (position and velocity) impact sensorimotor control (Pruszynski, J. A., & Scott, S. H. (2012). Experimental brain research, 218, 341-359.). Of course, it is appreciated that it is not possible to completely control all states when comparing movement and posture (i.e., muscle and limb velocity). However, using different perturbations differentially impacts muscle and limb states. Within this paper, the authors used very different force waveforms for movement perturbations (i.e., 12 N peak, bell-shaped, 0.7ms duration -> sudden force onset to push the limb; Figure 6A) and posture perturbations (i.e., 6N, 2s ramp up -> 3s hold -> sudden force release that resulted in limb movement; Figure 4) that would differentially impact muscle (and limb) states. Preloaded muscle (as in the posture perturbation) has a very different response compared to muscle that has little preload (as in the movement perturbations, where muscles that would resist a sudden lateral perturbation would likely be less activated since they are not contributing to the forward movement). Would the results hold if the same perturbation had been used for both posture and movement (e.g., 12 N pulse for both experiments)? It is recommended that the authors comment and discuss in the paper why they chose different perturbations and how that might impact the results. 

Response 2.2a. We agree that it can be impossible to completely control all states when comparing movement and posture. We would also like to stress that these perturbations were not designed so that responses are directly compared to each other (though of course there is an indirect comparison in the sense that we show influence of biases in one type of perturbation but not the other). Instead, Experiment 2 tried to implement a probe optimized for each motor control modality (moving vs. holding). However, the Reviewer has a point that the potential impact of differences between the perturbations is important to discuss in the paper.

The Reviewer points out two potentially interesting differences between the two perturbations. First, the magnitude (6N for the posture perturbation vs. 12N for the pulse perturbation); second, the presence of background load in the posture perturbation, in contrast to the pulse perturbation.

For the movement perturbation, we used a 12-N, 70ms pulse. This perturbation and scaled versions have been tested before in both control and patient populations (Smith et al., 2000; Fine and Thoroughman, 2006). For the holding perturbation, we used a background load to ensure that active holding control is engaged, and the duration of the probe (holding for about 5s) made using a stronger perturbation impractical –maintaining a background load at, say, 12N for that long could lead to increased fatigue.

The question raised by the Reviewer, whether the findings would be the same if the same, 12-N pulse were used to probe both moving and holding control, is interesting to investigate. We would expect the same qualitative findings (i.e. there would still be a connection between resting posture and active holding control when the latter were probed with a 12N pulse). Recent work provides more specific insight into what to expect. Our posture perturbation task is similar to the Unload Task in (Lowrey et al., 2019), whereby a background torque is released, whereas our pulse perturbation is more similar to their Load Task, whereby a torque is imposed against no background load (though it is a step perturbation rather than a pulse). Lowrey et al., 2019 find that their Unload task is harder than the Load task, with 2x the fraction of patient trials classified as failed (with failure defined as task performance being outside of the 95% confidence interval for controls), though there are still clear effects for the Load task. 

This suggests that the potential effects of using a pulse-like perturbation to probe posture control would likely be weaker in magnitude, all other things being equal. At the same time, however, the Load and Unload tasks in Lowrey et al., 2019 were perturbations of the same magnitude; it is thus also likely that the reduction in effect would be mitigated, or reversed, by the fact that we would be using a 12N instead of a 6N perturbation.

A relevant consequence of the Lowrey et al., 2019 findings is that the Unload paradigm is superior in its ability to detect impairment in static, posture perturbations, and thus provides a better signal to detect potential relationships with resting posture biases. This is not surprising, as a background load further engages the control of active holding, which what we were trying to probe in the first place.

But then why not use the same paradigm (preloading and release) for movement? There are two main reasons. First, requiring a background load throughout the experiment is unfeasible due to fatigue. Second, for the holding perturbation, we wanted to ensure that the postural control system is meaningfully engaged when the perturbation hits, hence we picked the background load. Were we to impose the same during moving – i.e. impose a lateral background load on the movement - we could be engaging posture control on top of movement control. This preloading would reduce the degree to which the pulse probe isolates movement control, and lead to intrusion of the posture control system in the movement task by design. This relates to what the Reviewer proposes in the comment below: preloading may result in postural biases i.e. engage posture control; see below where we argue this interpretation is within the scope of our conceptual model rather a counter to it.

We now explain the rationale behind our perturbation design in the Methods section (lines 211-220).

Relatedly, an alternative interpretation of the results is that preloading muscle for stroke patients, whether by supporting the weight of one's arm (experiment 1) or statically resisting a load prior to force release (experiment 2), leads to a greater postural force bias that can subsequently influence control. It is recommended that the authors comment on this. 

Response 2.2b. We find this interpretation valid, but we do not see how it meaningfully differs from the framework we propose. We already state that the RST may be tailored for both posture/holding control and the production of large forces (which would include muscle preloading):

“Thus, the accumulated evidence suggests that the RST could control posture and large force production in the upper limb.“ (lines 698-699 in the current version)

“the RST, in contrast, is weighted more towards slower postural control and generation of large isometric forces” (lines 724-726 in the current version)

And, we discuss other conditions where the RST is involved in large force production, such as power grip, and how these interact with the role of the RST in posture/holding control (lines 758-768 in the current version).

To better explain our model, we now provide the two examples mentioned by the reviewer along with our description of the proposed role for the RST (lines 726-727):

“…the RST, in contrast, is weighted more towards slower postural control and generation of large isometric forces (such as vertical forces for arm support, or horizontal forces for holding the arm still against a background load like in our posture/release perturbation trials).”

We note, however, that we find resting posture abnormalities even in the presence of arm support, suggesting the involvement of the RST in holding control even when the forces involved (and the need to preload the muscle) are small.

Reviewer #3 (Public Review): 

The authors attempt to dissociate differences in resting vs active vs perturbed movement biases in people with motor deficits resulting from stroke. The analysis of movement utilizes techniques that are similar to previous motor control in both humans and non-human primates, to assess impairments related to sensorimotor injuries. In this regard, the authors provide additional support to the extensive literature describing movement abnormalities in patients with hemiparesis both at rest and during active movement. The authors describe their intention to separate out the contribution of holding still at a position vs active movement as a demonstration that these two aspects of motor control are controlled by two separate control regimes.

Strengths: 

(1) The authors utilize a device that is the same or similar to devices previously used to investigate motor control of movement in normal and impaired conditions in humans and non-human primates. This allows comparisons to existing motor control studies. 

(2) Experiment 1 demonstrates resting flexion biases both in supported and unsupported forelimb conditions. These biases show a correlated relationship with FM-UE scores, suggesting that the degree of motor impairment and the degree of resting bias are related.

(3) The stroke patient participant population had a wide range of both levels of impairment and time since stroke, including both sub-acute and chronic cases allowing the results to be compared across impairment levels.

The authors describe several results from their study: 1. Postural biases were systematically toward the body (flexion) and increased with distance from the body (when the arm was more extended) and were stronger when the arm was unsupported. 2. These postural biases were correlated with FM-UE score. 3. They found no evidence of postural biases impacting movement, even when that movement was perturbed. 4. When holding a position at the end of a movement, if the position was perturbed opposite of the direction of bias, movement back to the target was improved compared to the perturbation in the direction of bias. Taken together, the authors suggest that there are at least two separate motor controls for tasks at rest versus with motion. Further, the authors propose that these results indicate that there is an imbalance between cortical control of movement (through the corticospinal tracts) and postural control (through the reticulospinal tract).

Response 3.1. Thank you for pointing out some of the strengths of our work and summarizing our findings. A minor clarification we would like to make, related to (3), is that, while our study did enroll two patients towards the end of the subacute stage (2-3 months), the rest of the population were at the chronic stage, at one year and beyond. We thus find it very unlikely that time after stroke was the primary driver of differences in impairment in the population we studied.

There are several weaknesses related to the interpretation of the results:

In Experiment 1, the participants are instructed to keep their limbs in a passive position after being moved. The authors show that, in the impaired limb, these resting biases are significantly higher when the limb is unsupported and increase when the arm is moved to a more extended position.

When supported by the air sled, the arm is in a purely passive position, not requiring the same antigravity response so will have less RST but also less CST involvement. While the unsupported task invokes more involvement of the reticulospinal tract (RST), it likely also has significantly higher CST involvement due to the increased difficulty and novelty of the task.

If there were an imbalance in CST regulating RST as proposed by the authors, the bias should be higher in the supported condition as there should be relatively less CST activation/involvement/ modulation leading to less moderating input onto the RST and introducing postural biases. In the unsupported condition, there is likely more CST involvement, potentially leading to an increased modulatory effect on RST. If the proportion of CST involvement significantly outweighs the RST activation in the unsupported task, then it isn't obvious that there is a clear differentiation of motor control. As the degree of resting force bias and FM-UE score are correlated, an argument could be made that they are both measuring the impairment of the CST unrelated to any RST output. If it is purely the balance of CST integrity compared to RST, then the degree of bias should have been the same in both conditions. In this idea of controller vs modulator, it is unclear when this switch occurs or how to weigh individual contributions of CST vs. extrapyramidal tracts. Further, it isn't clear why less modulation on the RST would lead only to abnormal flexion.

Response 3.2. Our model posits two mechanisms by which CST impairment would lead to increased RST involvement. The first – which is the one discussed by the Reviewer here - is a direct one, whereby weaker modulation of the RST by the CST leads to increased RST involvement. The second is an indirect one, whereby the incapacity of CST to drive sufficient motor output to deal with tasks eventually leads to increased RST drive.

The reviewer suggests it is likely that the unsupported task demands increased activation through both the CST and the RST. If that were the case, however, it would exaggerate the effects of CST/RST imbalance after stroke compared to healthy motor control: if task conditions (lack of support) required higher CST involvement, then CST damage would have an even larger effect. In turn, this would lead to even higher RST involvement and further diminishing the ability of CST to moderate RST. Thus, RST-driven biases would be higher in the unsupported condition.

And, given that the CST itself is damaged and has to deal with an even-increased RST activation, we would not expect that the proportion of CST involvement would outweigh RST activation, but the opposite. In fact, a series of relatively recent findings suggest just this. For example,

• Zaaimi et al., 2012  showed that unilateral CST lesions in monkeys lead to significant increases in the excitability of the contralesional RST (Zaaimi et al., 2012). Interestingly, this effect was present in flexors but not extensors, potentially explaining why less modulation and/or overactivation of the RST would primarily lead to abnormal flexion. 

• McPherson et al. (further discussed in point 2.A.23, by Reviewer 2 – Recommendations to the Authors) showed that, after stroke, contralesional activity (which would include the ipsilateral RST) increases relative to ipsilesional activity (which would include the contralateral CST)

(McPherson et al., 2018). The same study also provides evidence that FM-UE may primarily reflect RST-driven impairment. The ipsilateral(RST)/contralateral(CST) balance, expressed as a laterality index, correlated with FM-UE, with lower FM-UE for indices indicating higher RST involvement. (Interestingly, the slope of this relationship was steeper when the laterality of brain activation patterns was examined under tasks with less arm support, mirroring the steeper FM-UE vs resting bias slope when arm support is absent, as shown in our Figure 8).

• Wilkins et al., 2020 (Wilkins et al., 2020) found that providing less support (i.e. requiring increased shoulder abduction) increases ipsilateral activation (representing RST) relative to contralateral activation (representing CST).

This resting bias could be explained by an imbalance in the activation of flexors vs extensors which follows the results that this bias is larger as the arm is extended further, and/or in a disconnect in sensory integration that is overcome during active movement. Neither would necessitate separate motor control for holding vs active movement. 

Response 3.3. We do not think that either of these points necessarily argue against our model. First, the resting biases we observe are clearly pointed towards increased flexion, and can thus be seen as the outcome of an imbalance in the activation of flexors vs. extensors at rest. This imbalance between flexors/extensors can also be explained by the CST/RST imbalance posited by our conceptual model: in their study of CST lesions in the monkey, Zaaimi et al., 2012 found increased RST activation for flexors but not extensors, suggesting that RST over-involvement may specifically lead to flexor abnormalities (Zaaimi et al., 2012). Second, overcoming a disconnect in sensory integration may be one way the motor system switches between separate controllers; how this switch happens is not examined by our conceptual model.

In Experiment 2, the participants are actively moving to and holding at targets for all trials while being supported by the air sled. Even with the support, the paretic participants all showed start- and endpoint force biases around the movement despite not showing systematic deviations in force direction during active movement start or stop. There could be several factors that limit systematic deviations in force direction. The most obvious is that the measured biases are significantly higher when the limb is unsupported and by testing with a supported limb the authors are artificially limiting any effect of the bias.

Response 3.4. We do expect, in line with what the reviewer suggests, that any potential effects would be stronger in the unsupported condition. The decision to test active motor control with arm support was done as running the same Experiment 2 would pose challenges, particularly with our most impaired patients, given the duration of Experiment 2 (~2 hours, about 1 hour with each arm) and the expected fatigue that would ensue.

However, a key characteristic of our comparisons is that we are comparing Experiment 2 active control data under arm support, against Experiment 1 resting bias data also under arm support. While Experiment 1 measured biases without arm support as well, these are not used for this comparison. And, while resting biases are weaker with arm support, they are still clear and significant; yet they do not lead to detectable changes in active movement.

At the same time, we do not rule out that, if we were to repeat Experiment 2 without arm support, we could find some systematic deviation in the direction of resting bias in movement control. Our conceptual model, in fact, suggests that this may be the case, as we described in lines 618-620 of our original manuscript. The idea here is that, when arm support is not provided, the increased strength requirements lead to increased drive through the RST, to the point that posture control (and its abnormalities) spills into movement control (Figure 9). We now better clarify this position in our Discussion (lines 744-750):

“The interesting implication of this conceptual model is that synergies are in fact postural abnormalities that spill over into active movement when the CST can no longer modulate the increased RST activation that occurs when weight support is removed (i.e. resting biases may influence active reaching in absence of weight support). Supporting this idea, a study found increased ipsilateral activity (which primarily represents activation via the descending ipsilateral RST (Zaaimi et al., 2012)) when the paretic arm had reduced support compared to full support (McPherson et al., 2018).”

It is also possible that significant adaptation or plasticity with the CST or rubrospinal tracts could give rise to motor output that already accounts for any intrinsic resting bias.  

Response 3.5. This kind of adaptation – regardless of the tracts potentially involved – is an issue we examined in our experiment. As we talk about in our Results (lines 458-460 in the updated manuscript), with most of our patient population in the chronic stage, it could be likely that their motor system adapted to those biases to the point that movement planning took them into account, thereby limiting their effect. This motivated us to examine responses to unpredictable perturbations during movement (Figure 6) where we still find lack of an obvious effect of resting biases upon reaching control. We thus believe that our findings are not explained by this kind of adaptation, though we agree it would be of great interest for future work to compare resting biases and reaching control in acute vs. chronic stroke populations to examine the degree to which stroke patients adapt to these biases as they recover.

In any case, the results from the reaching phase of Experiment 2 do not definitively show that directional biases are not present during active reaching, just that the authors were unable to detect them with their design. The authors do acknowledge the limitations in this design (a 2D constrained task) in explaining motor impairment in 3D unconstrained tasks. 

Response 3.6. It is, of course, an inherent limitation of a negative finding is that it cannot be proven. What we show here is that, there is no hint of intrusion of resting posture abnormalities upon active movement in spite of these resting posture abnormalities being substantial and clearly demonstrated even under arm support. To allow for the maximum bandwidth to detect any such effects, we specifically chose to compare the most extreme instances (resting bias-wise) for each individual, and yet we did not find any relationship between biases and active reaching.

This suggests that, even if these biases could be in some form present during active movement, their effect would be minimal and thus limited in meaningfully explaining post-stroke impairment in active movement under arm support.

Note that, as we already discuss, our conceptual model (Figure 9) suggests that the degree to which directional biases would be present in active reaching may be influenced by arm support (or the specific movements examined – hence our limitation in not examining 3D movement). Thus we do not claim that this independence is absolute. Examples include the last line of the passage quoted right above, and the summary statement of our Discussion quoted below (lines 639-641):

“…which raises the possibility that the observed dissociation of movement and posture control for planar weight-supported movements may break down for unsupported 3D arm movements.”

Finally, we now more explicitly acknowledge that abnormal resting biases may influence active movement in the absence of arm support (see Response 3.4).

It would have been useful, in Experiment 2, to use FM-UE scores (and time from injury) as a factor to determine the relationship between movement and rest biases. Using a GLMM would have allowed a similar comparison to Experiment 1 of how impairment level is related to static perturbation responses. While not a surrogate for imaging tractography data showing a degree of CST involvement in stroke, FM-UE may serve as an appropriate proxy so that this perturbation at hold responses may be put into context relative to impairment.

Response 3.7. Here the Reviewer suggests we use FM-UE scores as a proxy for CST integrity. We do not think this analysis would be particularly helpful in our case for a number of reasons:

First, while FM-UE is a general measure of post-stroke impairment, it was designed to track - among other things - the emergence and resolution of abnormal synergies, a sign assumed to result from abnormally high RST outflow (McPherson et al., 2018; McPherson and Dewald, 2022). In line with this, the FM-UE scales with EMG-based measures of synergy abnormality (Bourbonnais et al., 1989). Impairments in dexterity, a sign associated with damage to the CST (Lawrence and Kuypers, 1968; Porter and Lemon, 1995; Duque et al., 2003), dissociate with synergy abnormalities when compared under arm support as we do here (Levin, 1996; Hadjiosif et al., 2022). This means that FM-UE would be a stronger proxy for RST activity and thus not a direct proxy for CST integrity particularly when one wants to dissociate RST-specific vs. CST-specific abnormalities. In fact, as we discuss in Response 3.2 above, there is a number of studies supporting this idea: for example, Zaaimi et al., 2012 show that relative RST activation – the balance between ipsilateral excitability, primarily reflecting RST, and contralateral excitability, primarily reflecting the CST, scales with FM-UE (Zaaimi et al., 2012).

Second, this kind of analysis would obscure within-individual effects, since FM-UE scores are, of course, assigned to each individual. This is the same issue as doing across-individual correlation analyses in general (see response 2.1b).Strong resting force bias would have opposite effects on opposing perturbations, averaging across subjects would occlude these effects.

Third, while FM-UE is a good measure of synergy abnormality, weakness alone could also give an abnormal FM-UE (Avni et al., 2024).

The Reviewer also suggests we use time from injury for this analysis. Time from injury can indeed potentially be an important factor. However, this analysis would not be appropriate for our dataset, since the effective variation in recovery stage within our population is limited: our sample is essentially chronic (only two patients were examined within the subacute stage – at 2 and 3 months after stroke - with everybody else examined more than a year after stroke) with the “positive” elements of their phenotype (and FM-UE itself) essentially plateaued (Twitchell, 1951; Cortes et al., 2017). We thus would not expect to see any meaningful effects of time from injury within our population. It would be an excellent question for future work to investigate both resting biases and their relationship to reaching in acute/subacute patients, and examine whether the trajectory of resting biases (both emergence and abatement due to recovery) follows the one for abnormal synergies.

It is not clear that even in the static perturbation trials that the hold (and subsequent move from perturbation) is being driven by reticulospinal projections. Given a task where ~20% of the trials are going to be perturbed, there is likely a significant amount of anticipatory or preparatory signaling from the CST. How does this balance with any proposed contribution that the RST may have with increased grip?

Response 3.8. We included our response to this as part of Response 3.2. In brief, while we cannot rule out that these tasks may recruit increased CST signaling, this would tend to increase, rather than reduce, the effects of post-stroke impairment: the requirement for increased signaling from a CST that is damaged would magnify the effects of this damage, in turn leading to increased recruitment of other tracts, such as the RST.

In general, the weakness of the interpretation of the results with respect to the CST/RST framework is that it is necessary to ascribe relative contributions of different tracts to different phases of movement and hold using limited or indirect measures. Barring any quantification of this data during these tasks, different investigators are likely to assess these contributions in different ways and proportions limiting the framework's utility.

Response 3.9. We believe that our Reponses 3.2-3.6 put our findings in fair perspective, and the edits undertaken based on the Reviewer’s comments have clarified our position as to how the dissociation between holding and moving control may break down. We do agree, however, that our framework would be strengthened by the use of direct measures of CST/RST connectivity in future research. We present our conceptual model as a comprehensive explanation of our findings and how they blend with current hypotheses regarding the role of these two tracts in motor control after stroke.  As such, it provides a blueprint towards future research that more directly measures or modulates CST and RST involvement, using tools such as tractography or non-invasive brain stimulation.

Recommendations for the authors:   

Reviewer #1 (Recommendations For The Authors):

L226 “…of this issue, we repeated the analysis of Figure 7F (a) by excluding these four patients…”.  Should this be three, based on the previous sentence? 

Response 1.A.1. Thank you for pointing this typo, which is now corrected. The analysis in question (Figure S1 in the original submission, now re-numbered as Figure S7-4), excluded the three patients mentioned in the previous sentence.

L254 “…the hand was held in a more distal position. The postural force biases were strongest when…”  Could this be "extended" rather than distal? See my later comment about the inadequate description of targets.

Response 1.A.2. The reviewer is correct that, the arm will tend to be more extended in the distal targets. However, since these positions were defined in extrinsic coordinates, we think the terms distal/proximal are also appropriate. In either case, we now clarify these definitions in the text (see Response 1.A.3 below).

L263 “…contained both distal and proximal targets, and, importantly, they were also the movement…”.  Distal/proximal targets were never described as part of the task. 

Response 1.A.3. We improved our description by (i) changing the wording above to “represented positions both distal and proximal to the body,”, (ii) doing the same in our Methods (line 175) and (iii) indicating distal/proximal targets in Figure 3A (bottom right of panel A).

L378 “…the pulse perturbation. We hypothesized that, should resting postural forces play a role, they…”  L379 “…would tend to reduce the effect of the pulse if they were in the opposite direction, and…”  Not really obvious why. A reduction in the displacement caused by a force pulse might be caused by different stiffness or viscosity, but not by a linear, time-invariant force bias. This situation is different from that of "moving the arm through a high-postural bias area vs. a low-postural bias area" where it would encounter time- (actually spatially) varying forces and varying amounts of displacement. Clarify the logic if this is a critical point.

Response 1.A.4. We thank the Reviewer for highlighting this point of potential confusion. We now clarify that these postural bias forces are neuromuscular in origin (Kanade-Mehta et al., 2023), and likely result from an expression of abnormal synergy, at least under static conditions. In this case, we hypothesized that force pulses acting against the gradient of the postural bias field would act to stretch the already active muscles, which would lead to a further increase in postural resistance due to inherent length-tension properties of active muscle. By contrast, force pulses acting along the gradient of the postural bias field would act to shorten the same active muscles, which would lead to a reduction in postural resistance. The data did not support this in the case of force pulses imposed during movement. We note, however, that similar effects would affect responses to static perturbations as well, wherein we do find an effect of resting biases. We now better explain this reasoning (lines 479482).

L466 “resting postural force). In short, our perturbations revealed that resting flexor biases switched  467 on after movement was over, providing evidence for separate control between moving” and 

L468 “holding still.”

I do not think the authors have presented clear evidence that forces, "switch on", implying the switch to a different controller which they posit. This could as easily be a nonlinear or time-varying property of a single controller (admittedly, the latter possibility overlaps broadly with their idea of distinct, interacting controllers). An example that the authors are certainly aware of is that of muscle "thixotropy" a purely peripheral mechanism due to the dynamics of crossbridge cycling that causes resting muscle to be stiffer than moving muscle, changing with a time constant of ~1-2 seconds. Neither this particular example nor changing levels of contraction (more likely during the unpredictable force perturbations) would be in the direction to explain the main observation here -- a point perhaps worth making, together with the stretch reflex comments. 

Response 1.A.5. Thank you for this perspective. Indeed, it might be that “switching on” represents a shift along a nonlinear property of the same controller: in the extreme, if this nonlinearity is a step (on/off) function, this single controller would be functionally identical to two separate controllers. We thus cannot tell if these controllers are distinct in the strict sense. What we argue here is that, no matter the underlying controller architecture - two distinct controllers or two distinct modes of the same controller - is that the control of reaching vs. holding can be functionally separable even after stroke. In line with this idea, we used a more nuanced phrasing (e.g. “separable functional modes for moving vs. holding”) throughout our manuscript, and we have now edited out a mention of “separate controllers” to be consistent with this.

Moreover, thank you for pointing out the example of thixotropy, showing how peripheral mechanisms could interact with central control. As you point out, this effect would not explain the main observation here: in fact, if stiffness were substantially higher during rest or holding (instead of moving) that would reduce the impact of the static perturbation, making it harder to detect any effects of resting biases compared to the moving perturbation case.

L480 “…during movement (Sukal et al., 2007). Yet, Experiment 2 found no relationship between resting…” L481”… postural force biases and active movement control. To further investigate this apparent…”  The methods of the two studies seem fairly similar, but this question warrants a more careful comparison. How did the size of the two workspaces compare? What about the magnitude of the exerted forces? The movement condition in this study was done with the limb entirely supported. Under that condition, the Sukal study also found fairly small effects of the range of motion.

Response 1.A.6. Sukal et al., 2007 did not directly measure exerted forces, but instead compared the active range of motion under different loading conditions. They used the extent of reach area to quantify the effect of abnormal synergies, with a more extended active range of motion signifying reduced effect of abnormal synergies. As the Reviewer points out, Sukal et al. found fairly small effects of synergies upon the range of motion when arm support was provided (the reach area for the paretic side was found to be about 85% of the nonparetic side under full arm support, though they were statistically significantly different, Figure 5 of their paper). They found increasing effect of synergies as arm support was reduced: on average, the reach area when participants had to fully support the arm was less than 50% the reach area when full arm support was given (comparing the 0% vs. 100% active support conditions [i.e. 100% vs. 0% external support] in their Figure 5). As we discuss in our paper, this effect of arm support upon synergy mirrors the one we found for resting postures.

To compare our workspace with the one in Sukal et al., we overlaid our workspace (the array of positions for which the posture biases were measured, for a typical participant from Experiment 1) on the one they used as shown in their Figure 4. Note that their figure only shows an example participant, and thus our ability to compare is limited by the fact that each participant can vary widely in terms of their impairment, and assumptions had to be made to prepare this overlay (e.g. that (0,0) represents the position of the right acromion point). 

For this example, and our assumptions, our workspace was smaller, with the main points of interest (red dots, the movement start/end points used for Experiment 2) within the Sukal et al. workspace. That our workspace is smaller is not surprising, given that the area in Sukal et al. represents the limit of what can be reached, and thus motor control *has* to be examined in a subset of that area.

Author response image 1.

Comparing the two study methodologies, however, suggests an advantage of measuring resting biases in terms of sensitivity and granularity: first, resting biases can be clearly detected even under arm support (something we point out in our Discussion, lines 715-717); second, they can measure abnormalities at any point in the workspace, rather than a binary within/without the reach area. The resting bias approach may thus be a more potent tool to probe the shared bias/synergy mechanisms we propose here.

Figure 2 

Needs color code. 

The red dots could be bigger.

Response 1.A.7. We have increased the size of the red dots and added a color code to explain the levels illustrated by the contours. We also expanded our caption to better explain this illustration.

Figure 3

Labeling is confusing. Drop the colored words (from both A and B), and stick to the color legend. Consider using open and filled symbols (and bars) to represent arm support or lack thereof. The different colored ovals are very hard to distinguish.

Response 1.A.8. We find these recommendations improve the readability of Figure 3 and we have thus adopted them - see updated Figure 3.

Figure 4

Not terribly necessary.  

Response 1.A.9. While this figure is indeed redundant based our descriptions in the text, we kept it as we believe it can be useful in clarifying the different stages of movement we examine.

Figure 5 

Tiny blue and green arrows are impossible to distinguish. 

Although the general idea is clear, E and H are not terribly intuitive.  Add distance scale bars for D-I. 

Response 1.A.10. For improved contrast, we now use red and blue (also in line with comment below regarding Figure 7), and switched to brighter colors in general. To make E and H more intuitive and easier to follow, we expanded the on-panel legend. Thank you for pointing out that distance scale bars are missing; we have now added them (panels EFHI).

Figure 6 

Panel E inset is too small. 

Response 1.A.11. We have now moved the inset to the right and enlarged it.

Figure 7 

Green and blue colors are not good. 

Response 1.A.12. For improved contrast, we now use red and blue.

Figure 8 

Delete or move to supplement? 

Response 1.A.13. We respectfully disagree. While the relationships on these data are also captured by the ANOVA, we believe these scatter plots offer a better overview of the relationships between force biases and FM-UE across different conditions.

Really minor

L113 “…participants' lower arm was supported using a custom-made air-sled (Figure 1C). Above the  participant's…” 

Response 1.A.14. We put the apostrophe after the s so to refer to participants in general (plural).

L117 ”…subject-produced forces on the handle were recorder using a 6-axis force transducer.”  recorded 

Response 1.A.14. Thank you for pointing out this error which we have now corrected.

L136 “…2013), Experiment 1 assessed resting postural forces by passively moving participants to>…”  The experiment did not move the participant. 

Response 1.A.15. We now fix this issue: “by having the robot passively move…”

L248 “…experiment blocks: two with each arm, with or without arm weight support (provided by an air experimental…”

Response 1.A.16. We have now corrected this.

L364 “…responses to mid-movement perturbations. In 1/3 of randomly selected reaching movements…”  Obviously, you mean 1/3 of all movements: "One-third of the reaching movements were chosen randomly"  

Response 1.A.17. We now clarify: “In 1/3 of reaching movements in Experiment 2, chosen randomly”. Also please note our response to Reviewer 2, point 10: we now report the exact number of trials for which each kind of perturbation was present.

L609 “Damage to the CST after stroke reduces its moderating influence upon the RST (Figure 9,…”  "its" refers to the subject, "Damage", not "CST".

Response 1.A.18. We have changed this to “Post-stroke damage to the CST reduces the moderating influence the CST has upon the RST”.

Reviewer #2 (Recommendations For The Authors):

(1) Throughout, the authors cleverly selected the most opposed and most aligned resting postural force biases to perform a within-subject analysis. However, this approach excludes a lot of data. The authors could perform an additional within-subject analysis. For each participant they could correlate lateral resting posture force bias to each dependent variable, utilizing all the trials of a participant. 

Response 2.A.1a. Thank you for your appreciating our analysis design, and suggesting additional analyses. We focused our within-subject analysis design on the most extreme instances, as we believe that this approach would offer the best opportunity to detect any potential effects of resting biases. We reasoned that, since resting biases tend to be relatively small for most locations in the workspace, taking all biases into account would inject a disproportionate amount of noise in our analysis, which would in turn diminish our ability to detect any potential relationships. This could be because small biases lead to small effects but also small biases may themselves be more likely to reflect measurement noise in the first place. Note that our study talks about separability of active reaching from resting abnormalities based on lack of relationships between the two. While one cannot definitely prove a negative, it is also important to take the approach that maximizes the ability to detect any such relationship if there were one. We believe taking the most extreme instances fulfills that role.

However, as the Reviewer points out, this approach also excludes a substantial amount of data. We agree that our findings could be further strengthened by exploring additional within-subject analyses that utilize all trials. Thus, following the reviewer’s suggestion, we estimated the sensitivity of each dependent variable to lateral resting posture force bias. Specifically, we estimated the slope of this relationship for each individual (separately for paretic and non-paretic data) using linear regression, and assessed whether the average slope is significant for each group (paretic data, non-paretic data, and control data).

This secondary analysis replicated our main findings: lack of relationship between posture biases and active reaching control (both for unperturbed and perturbed movement), and a significant relationship between posture biases and active holding control. In addition, in line with main point 2.1 by the reviewer, we performed the same analyses for non-paretic and control data. While there are no definitive conclusions to be made for these cases (as was likely, given that the resting force biases are smaller, as also pointed out by the Reviewer in 2.1) these data are worthy of discussion, with potentially interesting insights (for example, there are hints that the connection between resting biases and active holding control is present in the non-paretic arm as well, and may be explored in future research).

We have included these analyses in the supplementary materials, and we point to them in the main text. Specifically:

First, in line with our main analyses in Figure 5, we find no effect (the average slope is insignificant) for start and endpoint biases upon the corresponding reaching angles. This is now mentioned in lines 425-434 of the Results, and illustrated in Figure S5-2. There was a lack of effect for the non-paretic and control data as well.

Second, in line with our main analyses in Figure 6, we find no effect of start biases upon responses to the pulse (Figure S6-2, mentioned in lines 513-517 of the Results). As above, there was no effect of non-paretic or control data either.

And, finally, in line with our main analysis in Figure 7, we find an effect of resting biases upon performance for the static perturbation (Figure S7-2, mentioned in lines 578-586 of the Results). Interestingly, there is a suggestion that resting biases may affect static perturbation responses in the non-paretic data as well based on the relationship between posture bias and maximum deviation, but not the other two metrics. Given the lack of consistency of resting bias effects for all three different dependent variables examined, however, our current data are thus unable to give a definite answer as to whether there is the connection between resting biases and active holding control is also present in the non-paretic side. Our hypothesis is that, since resting abnormalities and their effects are the pathological over-manifestations of mechanisms inherent in the motor system in general, then such a relationship would exist. Answering this question, however, would require an experiment design better tailored to detect relationships in the non-paretic arm, where resting biases are weaker.

We thank the Reviewer for their suggestions and believe that these additional analyses provide a more complete picture of the data, and their consistency with our main results reinforces the message of the paper.

Then, they can report the percentage of participants that display significant correlations separately for the paretic, nonparetic, and control arms. 

Response 2.A.1b. We note that, even in cases where the average slope (across individuals) is significant, the individual slopes themselves are usually not significant, likely due to the large amount of noise for datapoints corresponding to weak resting biases. To further examine this, we performed additional analyses whereby we examined slopes by (a) pooling all participant data together (centered separately for each individual), and then (b) took a further step to normalize each participant’s data not only by centering but by also adjusting by each individual’s variability along each axis (i.e. assess the slope between z-scores of resting bias vs. z-scores of each dependent variable). These two analyses confirmed our finding that resting biases interacted with active motor control, with significant slopes between resting biases and outcome variables. (a) Pooling all data together: path to stabilization: p = 0.032; time to stabilization: p = 1.4x10-5; maximum deviation: p = 0.021. (b) Pooling and normalizing: path to stabilization: p = 0.0013; time to stabilization: p = 8.6x10-6; maximum deviation: p = 0.00056. The latter analysis showed even stronger connection between resting bias and active holding control, probably due to better accounting for differences in the range of resting biases across participants). For simplicity, however, we only provide the across-individual slope comparisons in the paper.

(2) An important aspect of all the analyses is that they rely heavily on estimates of the resting postural force bias. How stable are these resting postural force biases at the individual level? The authors could assess this by reporting within-subject variance for both the magnitude and direction of the resting postural force bias.

Response 2.A.2. Thank you for your suggestion. We now assess the individual-level variance in error across measurements for patients’ paretic data using an ANOVA: the variance that remains after all other factors (same probe location; same arm support condition; same participant) are taken into account. We found that individual level measurement variance explained a mere 9.0% of total variance for resting bias magnitude. (We note that the same figure was 20.2% for the non-paretic data, in line with the weaker average biases which would be more susceptible to noise). We now note this in the Methods, as part of the new subsection “Stability of resting posture bias measurements in Experiment 1” (lines 266-273).

(3) Does resting postural force bias influence hand movement immediately following force release from the postural perturbation? This could be assessed before any volitional responses by examining the velocity of the hand during the first 50 ms following the postural perturbation.

Response 2.A.3. The influence seems fairly rapid, within the first 100ms as shown to the right. Here we plot hand deviation in the direction of the perturbation for the most-opposed (red) vs. most-aligned (blue) instances to examine when these curves become different. The bottom plots show the difference between these two, whereas shading indicates SEM (note that these curves are referenced to the average deviation in the last 0.5 s before force release). The rightmost plots zoom in to make it easier to see how responses to the most opposed vs. most aligned instances diverge.

To detect the earliest post-perturbation timepoint for which this effect was significant, we performed paired t-tests at each timestep, and found that the two responses were systematically statistically different 95ms after perturbation onset onwards. For reference, the same method detected a response at 25ms for the most aligned instances and 40ms for the most opposed instances.

We have now added Supplementary Figure S7-4 with short commentary in the Supplementary Materials.

(4) Abstract. lines 7-9. At a glance (and when reading the manuscript linearly) this sentence is unclear. If the paretic arm is compromised across rest and movement, how does that afford the opportunity to address the relationship between reaching, stopping, and stabilizing when all could be impacted? It might be useful to specify that these factors may impacted differently relative to one another with stroke, providing an opportunity to better understand the differences between movement and postural control. 

Response 2.A.4. Thank you for pointing out this issue (also related to Reviewer 1’s point – Response 1.1). We have changed this to more clearly reflect our reasoning and highlight that the issue is that stroke can differentially impact reaching vs. holding, copied below:

“The paretic arm after stroke exhibits different abnormalities during rest vs. movement, providing an opportunity to ask whether control of these behaviors is independently affected in stroke.”

(5) Line 27. It is perhaps more appropriate to say conceptual model than simply 'model'.  

Response 2.A.5. Thank you for your suggestion, which we have adopted throughout the manuscript.

(6) Line 122-125. Figure 1A caption. The authors should specify that resting posture force biases occur when the limb or hand is physically constrained in a specific position. 

Response 2.A.6. Thank you for pointing this out – we have clarified the caption:

“If one were to physically constrain the hand in a position away from the resting posture, the torques involved in each component of the abnormal resting posture translate to a force on the hand (blue arrow);”

(7) Line 147. Why was the order not randomized or counterbalanced? 

Response 2.A.7. We prioritized paretic data, as the primary analyses and comparisons in our paper involved resting posture biases and active movement with the paretic arm. We note that our primary analyses, which rely on paretic-paretic comparisons, would not be affected by paretic vs. non-paretic ordering effects. However, ordering effects could potentially affect comparisons between paretic and non-paretic data. We now note the reasoning behind the absence of counterbalancing, and mention the potential limitation in interpreting paretic to non-paretic comparisons in lines 124-129 of the Methods.

(8) Line 172. 12N is the peak force of the pulse?

Response 2.A.8. The reviewer is correct; we have clarified our description (line 463 in the updated manuscript):

“a 70 ms bell-shaped force pulse which was 12N at its peak”

(9) Line 175. What is a clockwise pulse? Was the force vector rotating in direction over time so that it was always acting orthogonally to the movement, or did it always act leftwards or rightwards?

Response 2.A.9. The force vector was not rotating in direction over time. Here, we used clockwise/counterclockwise to indicate rightwards/leftwards with respect to the ideal movement direction – the line from start position to target (which is what we understand the Reviewer means by “always act rightwards or leftwards”). We have clarified the text to indicate this (lines 193-195):

…was applied by the robot lateral to the ideal movement direction (i.e. the direction formed between the center of the start position and the center of the target) after participants reached 2cm away from the starting position (Smith and Shadmehr, 2005; Fine and Thoroughman, 2006).

(10) Lines 177-182. It might be useful to explicitly mention the frequency of each of the perturbations, just for ease of the reader. 

Response 2.A.10. We have added this information to our Methods (lines 206-210):

Thus, in summary, each 96-movement block consisted of 64 unperturbed movements and 32 movements perturbed with a force pulse (16 clockwise, and 16 counter-clockwise). For 20 out of the 96 movements in each block, the hold period was extended to test the hold perturbation (4 trials for each of the 5 target locations, each one of the 4 trials testing one perturbation direction as shown in Figure 7C).

(11) Line 191. Lines 188-190. It would be useful to see a sample of several of these force traces over time (0-5s) that were used to make the average for a position. That would give insight into the stability of the forces of a participant for one of the postures. These traces could be shown in Figure 2.

Response 2.A.11. Thank you for your suggestion. We have added these panels to Figure 1, (as Figure 2 was already large). Each panel illustrates the three measurements taken at similar positions (closest to midline, distal from the body) and the same condition (paretic arm, with arm support given) for one participant (same participants as in Figure 2). Solid lines indicate the force on the x-axis (positive values indicate forces towards the left), whereas dashed lines indicate the force on the y-axis (positive values indicate forces towards the body). The shaded area indicates the part averaged in order to estimate the resting bias, illustrating how resting biases were relatively stable by the 2s mark. Note that these examples include one trial (blue traces in the third panel) which was rejected following visual inspection as described in Materials and Methods – Data Exclusion Criteria (“trials where forces appeared unstable and/or there was movement during the robot hold period”). We find this helpful as this illustrates (and motivates) one component of our methodology. 

(12) Line 196. Figure 1D (not 1E).  

Response 2.A.12. Thank you for catching this error, which we have now corrected.

(13) Line 215: The authors mentioned similar results. Were there any different results that impacted interpretation? Some evidence of this, similar to and in addition to Supplementary 1, would be helpful. 

Response 2.A.13. We repeated our analyses without these exclusion criteria, with no impact to the interpretation. We now include versions of the main outcome panels from Figures 5, 6, and 7 in the supplementary materials calculated without this outlier exclusion (Figures S5-E, S6-E, and S7-E, respectively). 

(14) Line 231: Perhaps better to explicitly state the furthest three positions are being across as the distal targets for the ANOVA. 

Response 2.A.14. Thank you for your suggestion. We now explicitly clarify this in line 276:

“distal targets [furthest three positions] vs. proximal targets [closest two positions]”

(15) Figure 3B, lines 265. Clearly, these are different, but the authors should report statistics. 

Response 2.A.15. We now report these numbers (lines 339-346 of the revised manuscript, which also include statistics related to bias direction as described in 2.A.17 below).

(16) Figure 2 should have a heat map scale.  

Response 2.A.16. We have now added this (also Response 1.A.7), including an explanation of what the heat map represents in the caption.

(17) Figure 3C: It would be useful to quantify and plot the direction of the resting force bias vector. 

Response 2.A.17. Thank you for your suggestion. We have expanded Figure 3 to include the average direction of the resting force bias vector (note the readjustment of colors following Reviewer 1’s comment: striped bars indicate No Support data, and full bars indicate Support data, with the colors being the same). The direction of the force bias vector, however, may not be very informative in cases where the magnitude is small (and the signal-to-noise ratio is small), whereas averaging the direction of the force bias vector across different positions for one participant may average out systematic variations in this direction across different locations. Nevertheless, the average direction appears generally towards the body (around -90°, or 6 o’clock) even in the non-paretic and control data (though the noise – as suggested by the size of the errorbars – is much higher in the latter cases, especially when the arm is supported). This is a (weak) suggestion that these resting biases may be present, though much subdued, in the nonparetic limb and healthy individuals; further work will be needed to elucidate this.

(18) Line 428. It is not significantly longer compared to controls. Can the authors slightly revise this sentence?

Response 2.A.18. We have revised this sentence (lines 529-532):

Patients showed impaired capacity to resist and recover from this perturbation (the abrupt release of the imposed force). The time to stabilization for the paretic side (0.94±0.05s) was longer compared to the non-paretic side (0.79±0.03s, p = 0.024) and controls (0.78±0.06s, though this was statistically marginal, p = 0.061) as shown in Figure 7E, left.

(19) Line 541. It is unclear how these data support the idea of three distinct controllers. Can the authors please clarify? 

Response 2.A.19. Here, we compared our findings to previous ideas about distinct controllers, and discuss a potential fusion of these ideas with ours. Specifically, we find that holding is distinct from both initial reaching and coming to a stop. Previous work argues that initial reaching and coming to a stop are themselves distinct (Ghez et al., 2007; Jayasinghe et al., 2022). Combining these two sets of arguments, we arrive at the possibility of three distinct controllers. 

(20) It would be useful if the authors provided a definition of synergy, as well as distinguishing between muscle and movement synergies. 

Response 2.A.20. We now provide this in lines 591-594:

Here, “synergies” refer to abnormal co-activation patterns across joints that manifest as the patient tries to move – for example, the elbow involuntarily flexing as the patient tries to abduct their shoulder (Twitchell, 1951; Brunnstrom, 1966). 

(21) Line 592-593. The wording of this sentence could be improved. 

Response 2.A.21. We have switched this sentence to active voice for more clarity:

Thus, while full weight support reduces both resting flexor biases and movement-related flexor synergies, this reduction seems more complete for synergies rather than resting biases.

(22) Figure 9. In the left column, it should read normal synergies and normal resting posture.  

Response 2.A.22. We intentionally used the same terminology, as the idea behind our conceptual model is that these patterns, which manifest as well-recognized abnormal synergies and abnormal resting postures in stroke, may be present in the healthy motor system as well, but kept in check by CST moderating the RST. At the same time, we recognize that, by definition, synergies and posture in controls are the “normal” reference point against which “abnormal” synergies and posture are defined after stroke. To clarify this issue, we thus decided to forgo the use of the terms “abnormal” in the figure, and instead refer to “synergistic movement ” and “synergistic resting posture”.

(23) Figure 9. With stroke, is RST upregulated, a decreased influence of CST, or both? All seem plausible.

Response 2.A.23a. We believe both can be happening. From previous work (e.g. McPherson et al., 2018) it seems safe to say that RST upregulation is the case, whereas one would also expect a decreased CST influence due to its damage due to the stroke. The relative weight of these influences would be interesting to elucidate in future work.

I have not read the paper, but did McPherson et al., 2018 test these different hypotheses?  

Response 2.A.23b. The main point of McPherson et al., 2018 is that increased synergy expression is due to increased RST involvement, rather than reduced CST influence. However, McPherson et al. do not show separate increases/reductions in RST/CST activity; they show that contralesional activity relative to ipsilesional activity is increased (using a laterality index). While it does seem that RST is upregulated in this case, this does not exclude the possibility that CST influence is reduced as well.

We also noticed that the citation itself, while mentioned in the text, was missing from the bibliography. This is now fixed.

For Figure 9, McPherson is cited as they provide evidence for the idea that RST involvement increases when arm support is decreased. This evidence is both direct (e.g. in their Figure 3 where they show that “Stroke participants exhibited increased activity in the contralesional (R) hemisphere as SABD loading increased” [i.e. arm support was reduced]) and indirect: they connect synergies to RST involvement, and also show increased synergies with reduced arm support (also shown multiple times previously). Both these arguments suggest that arm support reduces RST involvement. We have clarified the relevant sentence:

The interesting implication of this conceptual model is that synergies are in fact postural abnormalities that spill over into active movement when the CST can no longer modulate the increased RST activation that occurs when weight support is removed. Supporting this idea, McPherson et al. found increased ipsilateral activity (which primarily represents activation via the descending RST (Zaaimi et al., 2012)) when the paretic arm had reduced support compared to full support (McPherson et al., 2018).

Reviewer #3 (Recommendations For The Authors):

For Experiment 2, it is not immediately clear how the within-subject values are being pooled and compared across the different conditions. For instance, in the static perturbation trials, there are four blocks with 20 perturbation trials per block per arm (80 total per arm) with each location and direction once per block. For each participant, the comparison is between the location/direction that was most opposed (although this doesn't look accurately represented in Fig 7F). Therefore, the within-subject comparison is 4 trials per participant? Were these values averaged or pooled? It is a little odd that the SD for all the within-subjects trials are identical or nearly identical across conditions especially when looking at the example patient data in 7B and 7F.  

Response 3.A.1. For static perturbation trials, the within-subject comparison involves 8 trials per participant: 4 trials corresponding to the perturbation direction/position combination with resting bias most opposed to the perturbation, and 4 trials corresponding to the perturbation direction/position combination with resting bias most aligned with the perturbation. These values were averaged for each individual. We have expanded our methods to make this part of our data analysis clear (lines 284-296) for all types of comparisons (unperturbed movement, pulse perturbation, static perturbations – now referred to as “release perturbation”).

The across-subject SDs for the average resting forces for each one of these two conditions, shown in Figure 7F are indeed identical. This is due to how these two instances (most aligned vs. most resistive) were selected: because the perturbation directions come in pairs that exactly oppose each other (Figure 7B), if one were to select the position with the most opposing resting bias, that would mean that the combination with same position and the oppositely-directed perturbation would be the one with the most assistive resting bias. Hence the resting biases selected for the most opposing/assistive instances would be equal in magnitude and opposite to each other for each participant, as illustrated in Figure 7F, whereby the most-opposed bias for each individual is exactly opposite to the corresponding most-aligned bias for the same individual. We have added a brief commentary about this on the caption (lines 551-554), reproduced below:

Note how the most-opposed resting bias for each patient is equal and opposite to the their mostaligned resting bias. This is because the same resting bias, when projected along the direction of two oppositely-directed perturbations (illustrated in C), it would oppose one with the same magnitude it would align with the other.

Importantly, following suggestions by Reviewer 2 (see point 2.A.1), we now provide supplementary analyses that use the entirety of the relevant data, rather than the most extreme instances, which provide evidence supporting our main findings (Figures S5-2, S6-2, and S7-2).

The printed colors in Figure 3 are very muddled and hard to read/interpret, especially in panel A. 

Response 3.A.2. Thank you for pointing out this issue, also raised by Reviewer 1. We have adjusted the colors to be more distinct from each other and look clear both in print and on-screen, making use of dashed lines and stripes rather than different shades.

I think it would improve readability and interpretation if Figure 8 and the results related to FM-UE were contained within the description of results for Experiment 1.

Response 3.A.3. Thank you for this suggestion. This is actually a debate we had among ourselves earlier, and we can see merits to either ordering. It is very arguable that moving Figure 8 and the FMUE results within the rest of Experiment 1 may improve readability somewhat. However, we believe that presenting these results at the end better serves to illustrate the apparent paradox between the lack of direct connection between resting biases and active movement on one hand, and the relationship between resting biases and abnormal synergies on the other. We believe that this better sets the stage to present our conceptual model, which explains this paradox based on the role arm support plays in modulating the expression of both resting biases and abnormal synergies.

Additional changes/corrections not outlined above

Figure 1D displayed a right arm, but showed a target array (red dots) for a left arm paradigm. We now flip the target array shown for consistency.

We corrected Figure 6C, which accidentally used an earlier definition of settling time which was based on lateral stabilization throughout the entire movement, rather focus on the period immediately following the pulse. The intended definition of settling time (as we had described in the Methods, lines 204-206 of original submission) focuses on lateral corrections specific to the pulse (rather than corrections when the participant approaches the endpoint) and better matches the one for settling time for the release (static) perturbation trials. Note that this change did not affect the (lack of) relationship between settling time and resting force bias, both across individuals (correlation plots now in Figure S6-1) and within individuals (now shown in the right part of panel 6D). Also in panel C, an error in the scaling for the maximum lateral deviation in the pulse direction (right side of the panel) is also now corrected.

In addition, we made minor edits throughout the text to improve readability.

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eLife Assessment

This study provides important insights into the regulation of a retained intron in the mRNA coding for OGT, a process known to be regulated by the O-GlcNAc cycling system, and highlights the functional role of the splicing regulator SFSWAP. The evidence supporting the claims of the authors is convincing: the authors performed an elegant state-of-the-art CRISPR knockout strategy and sophisticated bioinformatic analysis to identify SFSWAP as a negative regulator of alternative splicing. The work will be of interest to researchers in the fields of splicing and glycobiology.

Reviewer #1 (Public review):

Summary:

Govindan and Conrad use a genome-wide CRISPR screen to identify genes regulating retention of intron 4 in OGT, leveraging an intron retention reporter system previously described (PMID: 35895270). Their OGT intron 4 reporter reliably responds to O-GlcNAc levels, mirroring the endogenous splicing event. Through a genome-wide CRISPR knockout library, they uncover a range of splicing-related genes, including multiple core spliceosome components, acting as negative regulators of OGT intron 4 retention. They choose to follow up on SFSWAP, a largely understudied splicing regulator shown to undergo rapid phosphorylation in response to O-GlcNAc level changes (PMID: 32329777). RNA-sequencing reveals that SFSWAP depletion not only promotes OGT intron 4 splicing but also broadly induces exon inclusion and intron splicing, affecting decoy exon usage. While this study offers interesting insights into intron retention and O-GlcNAc signaling regulation, the RNA sequencing experiments lack the essential controls needed to provide full confidence to the authors' conclusions.

Strengths:

(1) This study presents an elegant genetic screening approach to identify regulators of intron retention, uncovering core spliceosome genes as unexpected positive regulators of intron retention.

(2) The work proposes a novel functional role for SFSWAP in splicing regulation, suggesting that it acts as a negative regulator of splicing and cassette exon inclusion, which contrasts with expected SR-related protein functions.

(3) The authors suggest an intriguing model where SFSWAP, along with other spliceosome proteins, promotes intron retention by associating with decoy exons.

Weaknesses:

(1) The conclusions on SFSWAP impact on alternative splicing are based on cells treated with two pooled siRNAs for five days. This extended incubation time without independent siRNA treatments raises concerns about off-target effects and indirect effects from secondary gene expression changes, potentially limiting confidence in direct SFSWAP-dependent splicing regulation. Rescue experiments and shorter siRNA-treatment incubation times could address these issues.

(2) The mechanistic role of SFSWAP in splicing would benefit from further exploration. Key questions remain, such as whether SFSWAP directly binds RNA, specifically the introns and exons (including the decoy exons) it appears to regulate. Furthermore, given that SFSWAP phosphorylation is influenced by changes in O-GlcNAc signaling, it would be interesting to investigate this relationship further. While generating specific phosphomutants may not yield definitive insights due to redundancy and also beyond the scope of the study, the authors could examine whether distinct SFSWAP domains, such as the SR and SURP domains, which likely overlap with phosphorylation sites, are necessary for regulating OGT intron 4 splicing.

(3) Data presentation could be improved (specific suggestions are included in the recommendations section). Furthermore, Excel tables with gene expression and splicing analysis results should be provided as supplementary datasheets. Finally, a more detailed explanation of statistical analyses is necessary in certain sections.

Reviewer #2 (Public review):

Summary:

The paper describes an effort to identify the factors responsible for intron retention and alternate exon splicing in a complex system known to be regulated by the O-GlcNAc cycling system. The CRISPR/Cas9 system was used to identify potential factors. The bioinformatic analysis is sophisticated and compelling. The conclusions are of general interest and advance the field significantly.

Strengths:

(1) Exhaustive analysis of potential splicing factors in an unbiased screen.

(2) Extensive genome wide bioinformatic analysis.

(3) Thoughtful discussion and literature survey.

Weaknesses:

(1) No firm evidence linking SFSWA to an O-GlcNAc specific mechanism.

(2) Resulting model leaves many unanswered questions.

Reviewer #3 (Public review):

Summary:

The major novel finding in this study is that SFSWAP, a splicing factor containing an RS domain but no canonical RNA binding domain, functions as a negative regulator of splicing. More specifically, it promotes retention of specific introns in a wide variety of transcripts including transcripts from the OGT gene previously studied by the Conrad lab. The balance between OGT intron retention and OGT complete splicing is an important regulator of O-GlcNAc expression levels in cells.

Strengths:

An elegant CRISPR knockout screen employed a GFP reporter, in which GFP is efficiently expressed only when the OGT retained intron is removed (so that the transcript will be exported from the nucleus to allow for translation of GFP). Factors whose CRISPR knockdown causes decreased intron retention therefore increase GFP, and can be identified by sequencing RNA of GFP-sorted cells. SFSWAP was thus convincingly identified as a negative regulator of OGT retained intron splicing. More focused studies of OGT intron retention indicate that it may function by regulating a decoy exon previously identified in the intron, and that this may extend to other transcripts with decoy exons.

Weaknesses:

The mechanism by which SFSWAP represses retained introns is unclear, although some data suggests it can operate (in OGT) at the level of a recently reported decoy exon within that intron. Interesting/appropriate speculation about possible mechanisms are provided and will likely be the subject of future studies.

Overall the study is well done and carefully described but some figures and some experiments should be described in more detail.

eLife Assessment

This study provides useful findings about the effects of heterozygosity for Trio variants linked to neurodevelopmental and psychiatric disorders in mice. However, the strength of the evidence is limited and incomplete mainly because the experimental flow is difficult to follow, raising concerns about the conclusions' robustness. Clearer connections between variables, such as sex, age, behavior, brain regions, and synaptic measures, and more methodological detail on breeding strategies, test timelines, electrophysiology, and analysis, are needed to support their claims.

Reviewer #1 (Public review):

Summary:

This study explores how heterozygosity for specific neurodevelopmental disorder-associated Trio variants affects mouse behavior, brain structure, and synaptic function, revealing distinct impacts on motor, social, and cognitive behaviors linked to clinical phenotypes. Findings demonstrate that Trio variants yield unique changes in synaptic plasticity and glutamate release, highlighting Trio's critical role in presynaptic function and the importance of examining variant heterozygosity in vivo.

Strengths:

This study generated multiple mouse lines to model each Trio variant, reflecting point mutations observed in human patients with developmental disorders. The authors employed various approaches to evaluate the resulting behavioral, neuronal morphology, synaptic function, and proteomic phenotypes.

Weaknesses:

While the authors present extensive results, the flow of experiments is challenging to follow, raising concerns about the strength of the experimental conclusions. Additionally, the connection between sex, age, behavioral data, brain regions, synaptic transmission, and plasticity lacks clarity, making it difficult to understand the rationale behind each experiment. Clearer explanations of the purpose and connections between experiments are recommended. Furthermore, the methodology requires more detail, particularly regarding mouse breeding strategies, timelines for behavioral tests, electrophysiology conditions, and data analysis procedures.

Reviewer #2 (Public review):

Summary:

The authors generated three mouse lines harboring ASD, Schizophrenia, and Bipolar-associated variants in the TRIO gene. Anatomical, behavioral, physiological, and biochemical assays were deployed to compare and contrast the impact of these mutations in these animals. In this undertaking, the authors sought to identify and characterize the cellular and molecular mechanisms responsible for ASD, Schizophrenia, and Bipolar disorder development.

Strengths:

The establishment of TRIO dysfunction in the development of ASD, Schizophrenia, and Bipolar disorder is very recent and of great interest. Disorder-specific variants have been identified in the TRIO gene, and this study is the first to compare and contrast the impact of these variants in vivo in preclinical models. The impact of these mutations was carefully examined using an impressive host of methods. The authors achieved their goal of identifying behavioral, physiological, and molecular alterations that are disorder/variant specific. The impact of this work is extremely high given the growing appreciation of TRIO dysfunction in a large number of brain-related disorders. This work is very interesting in that it begins to identify the unique and subtle ways brain function is altered in ASD, Schizophrenia, and Bipolar disorder.

Weaknesses:

(1) Most assays were performed in older animals and perhaps only capture alterations that result from homeostatic changes resulting from prodromal pathology that may look very different.

(2) Identification of upregulated (potentially compensating) genes in response to these disorder-specific Trio variants is extremely interesting. However, a functional demonstration of compensation is not provided.

(3) There are instances where data is not shown in the manuscript. See "data not shown". All data collected should be provided even if significant differences are not observed.

I consider weaknesses 1 and 2 minor. While they would very interesting to explore, these experiments might be more appropriate for a follow-up study. I would recommend that the missing data in 3 should be provided in the supplemental material.

Author response:

eLife Assessment

This study provides useful findings about the effects of heterozygosity for Trio variants linked to neurodevelopmental and psychiatric disorders in mice. However, the strength of the evidence is limited and incomplete mainly because the experimental flow is difficult to follow, raising concerns about the conclusions' robustness. Clearer connections between variables, such as sex, age, behavior, brain regions, and synaptic measures, and more methodological detail on breeding strategies, test timelines, electrophysiology, and analysis, are needed to support their claims.

We appreciate the opportunity to address the constructive feedback provided by eLife and the reviewers. Below, we respond to the overall assessment and individual reviewers' comments, clarifying our experimental approach, addressing concerns, and providing additional details where necessary.

We thank the editors for highlighting the significance of our findings regarding the effects of Trio variant heterozygosity in mice. We acknowledge the feedback concerning the experimental flow and agree that clarity is paramount. To address these concerns:

(1) Connections between variables: We will revise the manuscript to explicitly outline and extend explanations and the relationships between sex, age, behavior, brain regions, and synaptic measures, ensuring that the rationale for each experiment and its relevance to the overall conclusions are improved.

(2) Methodological details: Our paper Methods section was formatted to be short with additional details provided in the Supplemental Methods section.  We will merge all into an extended section to improve clarity. We will also expand on our breeding strategies, test timelines, electrophysiological protocols, and data analysis methods in the revised Methods section. These additions aim to enhance the transparency and reproducibility of our study and to ensure full support of our conclusions.

(3) Experimental flow: We will revise and extend our results, methods, and discussion sections to clarify the rationale and experimental design to guide readers through the experimental sequence and rationale.

We are confident these revisions address the concerns raised and enhance the robustness and coherence of our findings.

Public Reviews:

Reviewer #1 (Public review):

Summary:

This study explores how heterozygosity for specific neurodevelopmental disorder-associated Trio variants affects mouse behavior, brain structure, and synaptic function, revealing distinct impacts on motor, social, and cognitive behaviors linked to clinical phenotypes. Findings demonstrate that Trio variants yield unique changes in synaptic plasticity and glutamate release, highlighting Trio's critical role in presynaptic function and the importance of examining variant heterozygosity in vivo.

Strengths:

This study generated multiple mouse lines to model each Trio variant, reflecting point mutations observed in human patients with developmental disorders. The authors employed various approaches to evaluate the resulting behavioral, neuronal morphology, synaptic function, and proteomic phenotypes.

Weaknesses:

While the authors present extensive results, the flow of experiments is challenging to follow, raising concerns about the strength of the experimental conclusions. Additionally, the connection between sex, age, behavioral data, brain regions, synaptic transmission, and plasticity lacks clarity, making it difficult to understand the rationale behind each experiment. Clearer explanations of the purpose and connections between experiments are recommended. Furthermore, the methodology requires more detail, particularly regarding mouse breeding strategies, timelines for behavioral tests, electrophysiology conditions, and data analysis procedures.

We appreciate the reviewer’s recognition of the novelty and comprehensiveness of our approach, particularly the generation of multiple mouse lines and our efforts to model Trio variant effects in vivo.

Weaknesses

(1) Experimental flow and rationale and connection between variables: We will expand on the connections between behavioral data, neuronal morphology, synaptic function, and proteomics in the Results and Discussion sections to clarify how each experiment informs the reasoning and the conclusions and to highlight the relationships between sex, age, behavior, and synaptic measures.

(2) Methodological details: Our paper Methods section was formatted to be short to fulfill word limits on the submitted version, with additional details provided in the Supplemental Methods section. We will merge our Methods and Supplemental Methods sections and expand on our breeding strategies, test timelines, electrophysiological protocols, and data analysis methods in the revised Methods section.  These additions aim to enhance the transparency and reproducibility of our study and to ensure full support of our conclusions.

Reviewer #2 (Public review):

Summary:

The authors generated three mouse lines harboring ASD, Schizophrenia, and Bipolar-associated variants in the TRIO gene. Anatomical, behavioral, physiological, and biochemical assays were deployed to compare and contrast the impact of these mutations in these animals. In this undertaking, the authors sought to identify and characterize the cellular and molecular mechanisms responsible for ASD, Schizophrenia, and Bipolar disorder development.

Strengths:

The establishment of TRIO dysfunction in the development of ASD, Schizophrenia, and Bipolar disorder is very recent and of great interest. Disorder-specific variants have been identified in the TRIO gene, and this study is the first to compare and contrast the impact of these variants in vivo in preclinical models. The impact of these mutations was carefully examined using an impressive host of methods. The authors achieved their goal of identifying behavioral, physiological, and molecular alterations that are disorder/variant specific. The impact of this work is extremely high given the growing appreciation of TRIO dysfunction in a large number of brain-related disorders. This work is very interesting in that it begins to identify the unique and subtle ways brain function is altered in ASD, Schizophrenia, and Bipolar disorder.

Weaknesses:

(1) Most assays were performed in older animals and perhaps only capture alterations that result from homeostatic changes resulting from prodromal pathology that may look very different.

(2) Identification of upregulated (potentially compensating) genes in response to these disorder-specific Trio variants is extremely interesting. However, a functional demonstration of compensation is not provided.

(3) There are instances where data is not shown in the manuscript. See "data not shown". All data collected should be provided even if significant differences are not observed.

I consider weaknesses 1 and 2 minor. While they would very interesting to explore, these experiments might be more appropriate for a follow-up study. I would recommend that the missing data in 3 should be provided in the supplemental material.

We are grateful for the reviewer’s recognition of our study’s significance and methodological rigor. The acknowledgment of Trio dysfunction as a novel and impactful area of research is deeply appreciated.

Weaknesses: 

We agree that focusing on older animals may limit insights into early-stage pathophysiology. However, given the goal of this study was to examine the functional impacts of Trio heterozygosity at an adolescent stage and to reveal the ultimate impact of these alleles on synaptic function, we believe the choice of animal age aligns with our objectives. We agree that future studies of earlier developmental stages will be beneficial and complement these findings.

Functional compensation: In this study, we tested functional compensation through rescue experiments in +/K1431M brain slices using a Rac1-specific inhibitor, NSC, which prevents its activation by Trio or Tiam1. Our findings strongly suggest that increased Rac1 activity, attributed to the proposed compensation, drives the deficiency in neurotransmitter release. Furthermore, this deficiency can be normalized by direct Rac1 inhibition.

Data not shown: We will incorporate all previously shown data into the Supplemental Materials, even when results are nonsignificant. We agree that this ensures full transparency and facilitates a more comprehensive evaluation of our findings.

eLife Assessment

This study investigates the fundamental role of polyunsaturated fatty acids (PUFAs) in membrane biology, using a unique model to perform a thorough genetic screen that highlights that PUFA synthesis defects cannot be compensated for by mutations in other pathways. While the data are solid and generally support the claims, additional experimental validation or more detailed descriptions of their results would strengthen the broader conclusions. This study will appeal to researchers in membrane biology, lipid metabolism, and C. elegans genetics.

Reviewer #1 (Public review):

Summary:<br /> This study addresses the roles of polyunsaturated fatty acids (PUFAs) in animal physiology and membrane function. A C. elegans strain carrying the fat-2(wa17) mutation possess a very limited ability to synthesize PUFAs and there is no dietary input because the E. coli diet consumed by lab grown C. elegans does not contain any PUFAs. The fat-2 mutant strain was characterized to confirm that the worms grow slowly, have rigid membranes, and have a constitutive mitochondrial stress response. The authors showed that chemical treatments or mutations known to increase membrane fluidity did not rescue growth defects. A thorough genetic screen was performed to identify genetic changes to compensate for the lack of PUFAs. The newly isolated suppressor mutations that compensated for FAT-2 growth defects included intergenic suppressors in the fat-2 gene, as well as constitutive mutations in the hypoxia sensing pathway components EGL-9 and HIF-1, and loss of function mutations in ftn-2, a gene encoding the iron storage protein ferritin. Taken together, these mutations lead to the model that increased intracellular iron, an essential cofactor for fatty acid desaturases, allows the minimally functional FAT-2(wa17) enzyme to be more active, resulting in increased desaturation and increased PUFA synthesis.

Strengths:<br /> (1) This study provides new information further characterizing fat-2 mutants. The authors measured increased rigidity of membranes compared to wild type worms, however this rigidity is not able to be rescued with other fluidity treatments such as detergent or mutants. Rescue was only achieved with polyunsaturated fatty acid supplementation.<br /> (2) A very thorough genetic suppressor screen was performed. In addition to some internal fat-2 compensatory mutations, the only changes in pathways identified that are capable of compensating for deficient PUFA synthesis was the hypoxia pathway and the iron storage protein ferritin. Suppressor mutations included an egl-9 mutation that constitutively activates HIF-1, and Gain of function mutations in hif-1 that are dominant. This increased activity of HIF conferred by specific egl-9 and hif-1 mutations lead to decreased expression of ftn-2. Indeed, loss of ftn-2 leads to higher intracellular iron. The increased iron apparently makes the FAT-2 fatty acid desaturase enzyme more active, allowing for the production of more PUFAs.<br /> (3) The mutations isolated in the suppressor screen show that the only mutations able to compensate for lack of PUFAs were ones that increased PUFA synthesis by the defective FAT-2 desaturase, thus demonstrating the essential need for PUFAs that cannot be overcome by changes in other pathways. This is a very novel study, taking advantage of genetic analysis of C. elegans, and it confirms the observations in humans that certain essential PUFAs are required for growth and development.<br /> (4) Overall, the paper is well written, and the experiments were carried out carefully and thoroughly. The conclusions are well supported by the results.

Weaknesses:<br /> Overall, there are not many weaknesses. The main one I noticed is that the lipidomic analysis shown in Figs 3C, 7C, S1 and S3. Whie these data are an essential part of the analysis and provide strong evidence for the conclusions of the study, it is unfortunate that the methods used did not enable the distinction between two 18:1 isomers. These two isomers of 18:1 are important in C. elegans biology, because one is a substrate for FAT-2 (18:1n-9, oleic acid) and the other is not (18:1n-7, cis vaccenic acid). Although rarer in mammals, cis-vaccenic acid is the most abundant fatty acid in C. elegans and is likely the most important structural MUFA. The measurement of these two isomers is not essential for the conclusions of the study, but the manuscript should include a comment about the abundance of oleic vs vaccenic acid in C. elegans (authors can find this information, even in the fat-2 mutant, in other publications of C. elegans fatty acid composition). Otherwise, readers who are not familiar with C. elegans might assume the 18:1 that is reported is likely to be mainly oleic acid, as is common in mammals.

Other suggestions to authors to improve the paper:<br /> (1) The title could be less specific; it might be confusing to readers to include the allele name in the title.<br /> (2) There are two errors in the pathway depicted in Figure 1A. The16:0-16:1 desaturation can be performed by FAT-5, FAT-6, and FAT-7. The 18:0-18:1 desaturation can only be performed by FAT-6 and FAT-7

Reviewer #2 (Public review):

Summary:<br /> The authors use a genetic screen in C. elegans to investigate the physiological roles of polyunsaturated fatty acids (PUFAs). They screen for mutations that rescue fat-2 mutants, which have strong reductions in PUFAs. As a result, either mutations in fat-2 itself, or mutations in genes involved in the HIF-1 pathway, were found to rescue fat-2 mutants.

Strengths:<br /> As C. elegans can produce PUFAs de novo as essential lipids, the genetic model is well suited to study the fundamental roles of PUFAs, and the results are very interesting. The genetic screen finds mutations in convergent pathways, suggesting that it has reached near-saturation. The link between the HIF-1 pathway and lipid unsaturation is very interesting. As many of the mutations found to rescue fat-2 mutants are of gain-of-function, it is unlikely that similar discoveries could have been made with other approaches like genome-wide CRISPR screenings, making the current study distinctive.

Weaknesses:<br /> The authors make very important statements, but some are not sufficiently supported by data. In page 5, they conclude that membrane rigidity is a minor cause of fat-2 mutant defects, which is a relevant observation regarding why PUFAs are important. However, they use treatments that have rescued fluidity in another mutant (paqr-2), but do not test if they have the same fluidifying effects in fat-2 mutants.

The screening results seem to converge into the HIF-1 pathway, which is hypothetically correct according to the literature. However, the authors do not validate this hypothesis, which is a critical limitation, especially because many of the mutations they obtained seem to be of gain-of-function. Therefore, without experimental testing, it cannot be concluded that the mutations have the expected effect on the HIF-1 pathway.

In some of the mutants, the rescues in lipid compositions seem to be weak, and it is arguable whether phenotypic rescues are really via a restoration in lipid compositions.

The hypothesis linking iron homeostasis and the rescue of fat-2 mutants is interesting, but the data of rescue by iron repletion seem to be against it. The results might be due to the inefficiency in iron repletion, as the authors suggest, but this has not been formally addressed.

Therefore, the authors propose multiple very interesting and important hypotheses, but experimental validations remain limited.

Author response:

We thank the editors at eLife and the reviewers for the care with which our mansucript has been reviewed and the constructive feedback that we have received. Both reviewers viewed the manuscript positively and in particular praised the merits of the forward genetic screen that led to the discovery of a new link between the HIF-1 pathway and fatty acid desaturation.

We agree with all points by Reviewer #1. We will modify our manuscript to clarify that two types of C18:1 fatty acids are present in our lipidomics, and that the majority is likely vaccenic acid that is not a FAT-2 substrate. The title will be modified and Fig. 1A corrected.

All points raised by Reviewer #2 are also valid and we will try to address most of them experimentally, though not always as suggested. In particular, we plan to use FRAP to verify that membrane-fluidizing treatments are effective in the fat-2 mutant. We also plan to use qPCR to test whether the novel egl-9(lof) and hif-1(gof) alleles lead to the expected downregulation of ftn-2. We note that the pathway connecting EGL-9, HIF-1 and FTN-2 is well supported by published work and that the alleles isolated in our screen are consistent with it, with the addition that FAT-2 is likely a regulated outcome of FTN-2 inhibition/mutation. We also plan to monitor FAT-2 protein levels using Western blots and thus provide more clarity about the mechanism of action of the novel fat-2(wa17) suppressors. The manuscript will be modified to tone down interpretations not directly supported by experiments.

eLife Assessment

This paper provides a valuable contribution to our understanding of how adenosine acts as a signal of nutrient insufficiency and extends this idea to suggest that adenosine is released by metabolically active cells in proportion to the activity of methylation events. Convincing data support this idea. The authors use metabolic tracing approaches to identify the biochemical pathways that contribute to the regulation of adenosine levels and the S-adenosylmethionine cycle in Drosophila larval hemocytes in response to wasp egg infection.

Reviewer #1 (Public review):

Summary:

In this article, Nedbalova et al. investigate the biochemical pathway that acts in circulating immune cells to generate adenosine, a systemic signal that directs nutrients toward the immune response, and S-adenosylmethionine (SAM), a methyl donor for lipid, DNA, RNA, and protein synthetic reactions. They find that SAM is largely generated through the uptake of extracellular methionine, but that recycling of adenosine to form ATP contributes a small but important quantity of SAM in immune cells during the immune response. The authors propose that adenosine serves as a sensor of cell activity and nutrient supply, with adenosine secretion dominating in response to increased cellular activity. Their findings of impaired immune action but rescued larval developmental delay when the enzyme Ahcy is knocked down in hemocytes are interpreted as due to effects on methylation processes in hemocytes and reduced production of adenosine to regulate systemic metabolism and development, respectively. Overall this is a strong paper that uses sophisticated metabolic techniques to map the biochemical regulation of an important systemic mediator, highlighting the importance of maintaining appropriate metabolite levels in driving immune cell biology.

Strengths:

The authors deploy metabolic tracing - no easy feat in Drosophila hemocytes - to assess flux into pools of the SAM cycle. This is complemented by mass spectrometry analysis of total levels of SAM cycle metabolites to provide a clear picture of this metabolic pathway in resting and activated immune cells.

The experiments show that the recycling of adenosine to ATP, and ultimately SAM, contributes meaningfully to the ability of immune cells to control infection with wasp eggs.

This is a well-written paper, with very nice figures showing metabolic pathways under investigation. In particular, the italicized annotations, for example, "must be kept low", in Figure 1 illustrate a key point in metabolism - that cells must control levels of various intermediates to keep metabolic pathways moving in a beneficial direction.

Experiments are conducted and controlled well, reagents are tested, and findings are robust and support most of the authors' claims.

Weaknesses:

The authors posit that adenosine acts as a sensor of cellular activity, with increased release indicating active cellular metabolism and insufficient nutrient supply. It is unclear how generalizable they think this may be across different cell types or organs.

The authors extrapolate the findings in Figure 3 of decreased extracellular adenosine in ex vivo cultures of hemocytes with knockdown of Ahcy (panel B) to the in vivo findings of a rescue of larval developmental delay in wasp egg-infected larvae with hemocyte-specific Ahcy RNAi (panel C). This conclusion (discussed in lines 545-547) should be somewhat tempered, as a number of additional metabolic abnormalities characterize Ahcy-knockdown hemocytes, and the in vivo situation may not mimic the ex vivo situation. If adenosine (or inosine) measurements were possible in hemolymph, this would help bolster this idea. However, adenosine at least has a very short half-life.

Reviewer #2 (Public review):

Summary:

In this work, the authors wish to explore the metabolic support mechanisms enabling lamellocyte encapsulation, a critical antiparasitic immune response of insects. They show that S-adenosylmethionine metabolism is specifically important in this process through a combination of measurements of metabolite levels and genetic manipulations of this metabolic process.

Strengths:

The metabolite measurements and the functional analyses are generally very strong and clearly show that the metabolic process under study is important in lamellocyte immune function.

Weaknesses:

The gene expression data are a potential weakness. Not enough is explained about how the RNAseq experiments in Figures 2 and 4 were done, and the representation of the data is unclear. The paper would also be strengthened by the inclusion of some measure of encapsulation effectiveness: the authors show that manipulation of the S-adenosylmethionine pathway in lamellocytes affects the ability of the host to survive infection, but they do not show direct effects on the ability of the host to encapsulate wasp eggs.

Reviewer #3 (Public review):

Summary:

The authors of this study provide evidence that Drosophila immune cells show upregulated SAM transmethylation pathway and adenosine recycling upon wasp infection. Blocking this pathway compromises the lamellocyte formation, developmental delay, and host survival, suggesting its physiological relevance.

Strengths:

Snapshot quantification of the metabolite pool does not provide evidence that the metabolic pathway is active or not. The authors use an ex vivo isotope labelling to precisely monitor the SAM and adenosine metabolism. During infection, the methionine metabolism and adenosine recycling are upregulated, which is necessary to support the immune reaction. By combining the genetic experiment, they successfully show that the pathway is activated in immune cells.

Weaknesses:

The authors knocked down Ahcy to prove the importance of SAM methylation pathway. However, Ahcy-RNAi produces a massive accumulation of SAH, in addition to blocking adenosine production. To further validate the phenotypic causality, it is necessary to manipulate other enzymes in the pathway, such as Sam-S, Cbs, SamDC, etc. The authors do not demonstrate how infection stimulates the metabolic pathway given the gene expression of metabolic enzymes is not upregulated by infection stimulus.

Author response:

We would like to thank the editors and reviewers for reviewing our work, for finding it valuable supported by convincing data, which we greatly appreciate, but also for identifying the weaknesses of the manuscript. We plan to address these weaknesses in the revised version, briefly as follows:

(1) In the Discussion, we will elaborate more on a possible generalization of our results, while being aware of the limited space in this experimental paper and therefore intend to address this in more detail and comprehensively in a subsequent perspective article.

(2) In the Discussion, we will more clearly address the limitations of our work, in particular the difference between the measurement of extracellular adenosine production ex vivo and the actual production in vivo, where the measurement is indeed very challenging, and also the limitations of manipulating the SAM pathway only at the Ahcy level.

(3) We will describe in detail and complement the supplementary RNAseq data. The RNAseq data have already been described in detail in our previous paper (doi.org/10.1371/journal.pbio.3002299), but we agree with the reviewers that we should describe the necessary details again here.

(4) We will fill in the missing data on encapsulation efficiency; we agree that it was unfortunate to omit them.

(5) We will supplement the data with methyltransferase expressions and better describe the changes in expression of some SAM pathway genes, which, especially with methyltransferase expressions, also support stimulation of this pathway by changes in expression. Although the goal of this work was to test by 13C-labeling whether SAM pathway activity is upregulated, not to analyze how the activity is regulated, we certainly agree that an explanation of possible regulation, especially in the context of the enzyme expressions we show, should be included in our work.

eLife Assessment

This valuable study investigates the neural basis of causal inference of illness, suggesting that it relies on semantic networks specific to living things in the absence of a generalized representation of causal inference across domains. However, the evidence remains incomplete due to some unjustified design and analysis choices. Moreover, the authors do not fully exploit the potential of multivariate fMRI analyses to rigorously test their main hypothesis.

Reviewer #1 (Public review):

Summary:

In this study, the authors aim to understand the neural basis of implicit causal inference, specifically how people infer causes of illness. They use fMRI to explore whether these inferences rely on content-specific semantic networks or broader, domain-general neurocognitive mechanisms. The study explores two key hypotheses: first, that causal inferences about illness rely on semantic networks specific to living things, such as the 'animacy network,' given that illnesses affect only animate beings; and second, that there might be a common brain network supporting causal inferences across various domains, including illness, mental states, and mechanical failures. By examining these hypotheses, the authors aim to determine whether causal inferences are supported by specialized or generalized neural systems.

The authors observed that inferring illness causes selectively engaged a portion of the precuneus (PC) associated with the semantic representation of animate entities, such as people and animals. They found no cortical areas that responded to causal inferences across different domains, including illness and mechanical failures. Based on these findings, the authors concluded that implicit causal inferences are supported by content-specific semantic networks, rather than a domain-general neural system, indicating that the neural basis of causal inference is closely tied to the semantic representation of the specific content involved.

Strengths:

(1) The inclusion of the four conditions in the design is well thought out, allowing for the examination of the unique contribution of causal inference of illness compared to either a different type of causal inference (mechanical) or non-causal conditions. This design also has the potential to identify regions involved in a shared representation of inference across general domains.

(2) The presence of the three localizers for language, logic, and mentalizing, along with the selection of specific regions of interest (ROIs), such as the precuneus and anterior ventral occipitotemporal cortex (antVOTC), is a strong feature that supports a hypothesis-driven approach (although see below for a critical point related to the ROI selection).

(3) The univariate analysis pipeline is solid and well-developed.

(4) The statistical analyses are a particularly strong aspect of the paper.

Weaknesses:

Based on the current analyses, it is not yet possible to rule out the hypothesis that inferring illness causes relies on neurocognitive mechanisms that support causal inferences irrespective of their content, neither in the precuneus nor in other parts of the brain.

(1) The authors, particularly in the multivariate analyses, do not thoroughly examine the similarity between the two conditions (illness-causal and mechanical-causal), as they are more focused on highlighting the differences between them. For instance, in the searchlight MVPA analysis, an interesting decoding analysis is conducted to identify brain regions that represent illness-causal and mechanical-causal conditions differently, yielding results consistent with the univariate analyses. However, to test for the presence of a shared network, the authors only perform the Causal vs. Non-causal analysis. This analysis is not very informative because it includes all conditions mixed together and does not clarify whether both the illness-causal and mechanical-causal conditions contribute to these results.

(2) To address this limitation, a useful additional step would be to use as ROIs the different regions that emerged in the Causal vs. Non-causal decoding analysis and to conduct four separate decoding analyses within these specific clusters:<br /> (a) Illness-Causal vs. Non-causal - Illness First;<br /> (b) Illness-Causal vs. Non-causal - Mechanical First;<br /> (c) Mechanical-Causal vs. Non-causal - Illness First;<br /> (d) Mechanical-Causal vs. Non-causal - Mechanical First.<br /> This approach would allow the authors to determine whether any of these ROIs can decode both the illness-causal and mechanical-causal conditions against at least one non-causal condition.

(3) Another possible analysis to investigate the existence of a shared network would be to run the searchlight analysis for the mechanical-causal condition versus the two non-causal conditions, as was done for the illness-causal versus non-causal conditions, and then examine the conjunction between the two. Specifically, the goal would be to identify ROIs that show significant decoding accuracy in both analyses.

(4) Along the same lines, for the ROI MVPA analysis, it would be useful not only to include the illness-causal vs. mechanical-causal decoding but also to examine the illness-causal vs. non-causal conditions and the mechanical-causal vs. non-causal conditions. Additionally, it would be beneficial to report these data not just in a table (where only the mean accuracy is shown) but also using dot plots, allowing the readers to see not only the mean values but also the accuracy for each individual subject.

(5) The selection of Regions of Interest (ROIs) is not entirely straightforward:<br /> In the introduction, the authors mention that recent literature identifies the precuneus (PC) as a region that responds preferentially to images and words related to living things across various tasks. While this may be accurate, we can all agree that other regions within the ventral occipital-temporal cortex also exhibit such preferences, particularly areas like the fusiform face area, the occipital face area, and the extrastriate body area. I believe that at least some parts of this network (e.g., the fusiform gyrus) should be included as ROIs in this study. This inclusion would make sense, especially because a complementary portion of the ventral stream known to prefer non-living items (i.e., anterior medial VOTC) has been selected as a control ROI to process information about the mechanical-causal condition. Given the main hypothesis of the study - that causal inferences about illness might depend on content-specific semantic representations in the 'animacy network' - it would be worthwhile to investigate these ROIs alongside the precuneus, as they may also yield interesting results.

(6) Visual representation of results:<br /> In all the figures related to ROI analyses, only mean group values are reported (e.g., Figure 1A, Figure 3, Figure 4A, Supplementary Figure 6, Figure 7, Figure 8). To better capture the complexity of fMRI data and provide readers with a more comprehensive view of the results, it would be beneficial to include a dot plot for a specific time point in each graph. This could be a fixed time point (e.g., a certain number of seconds after stimulus presentation) or the time point showing the maximum difference between the conditions of interest. Adding this would allow for a clearer understanding of how the effect is distributed across the full sample, such as whether it is consistently present in every subject or if there is greater variability across individuals.

(7) Task selection:<br /> (a) To improve the clarity of the paper, it would be helpful to explain the rationale behind the choice of the selected task, specifically addressing: (i) why an implicit inference task was chosen instead of an explicit inference task, and (ii) why the "magic detection" task was used, as it might shift participants' attention more towards coherence, surprise, or unexpected elements rather than the inference process itself.<br /> (b) Additionally, the choice to include a large number of catch trials is unusual, especially since they are modeled as regressors of non-interest in the GLM. It would be beneficial to provide an explanation for this decision.

Reviewer #2 (Public review):

Summary:

In this study, the authors hypothesize that "causal inferences about illness depend on content-specific semantic representations in the animacy network". They test this hypothesis in an fMRI task, by comparing brain activity elicited by participants' exposure to written situations suggesting a plausible cause of illness with brain activity in linguistically equivalent situations suggesting a plausible cause of mechanical failure or damage and non-causal situations. These contrasts identify PC as the main "culprit" in a whole-brain univariate analysis. Then the question arises of whether the content-specificity has to do with inferences about animates in general, or if there are some distinctions between reasoning about people's bodies versus mental states. To answer this question, the authors localize the mentalizing network and study the relation between brain activity elicited by Illness-Causal > Mech-Causal and Mentalizing > Physical stories. They conclude that inferring about the causes of illness partially differentiates from reasoning about people's states of mind. The authors finally test the alternative yet non-mutually exclusive hypothesis that both types of causal inferences (illness and mechanical) depend on shared neural machinery. Good candidates are language and logic, which justifies the use of a language/logic localizer. No evidence of commonalities across causal inferences versus non-causal situations is found.

Strengths:

(1) This study introduces a useful paradigm and well-designed set of stimuli to test for implicit causal inferences.

(2) Another important methodological advance is the addition of physical stories to the original mentalizing protocol.

(3) With these tools, or a variant of these tools, this study has the potential to pave the way for further investigation of naïve biology and causal inference.

Weaknesses:

(1) This study is missing a big-picture question. It is not clear whether the authors investigate the neural correlates of causal reasoning or of naïve biology. If the former, the choice of an orthogonal task, making causal reasoning implicit, is questionable. If the latter, the choice of mechanical and physical controls can be seen as reductive and problematic.

(2) The rationale for focusing mostly on the precuneus is not clear and this choice could almost be seen as a post-hoc hypothesis.

(3) The choice of an orthogonal 'magic detection' task has three problematic consequences in this study:<br /> (a) It differs in nature from the 'mentalizing' task that consists of evaluating a character's beliefs explicitly from the corresponding story, which complicates the study of the relation between both tasks. While the authors do not compare both tasks directly, it is unclear to what extent this intrinsic difference between implicit versus explicit judgments of people's body versus mental states could influence the results.<br /> (b) The extent to which the failure to find shared neural machinery between both types of inferences (illness and mechanical) can be attributed to the implicit character of the task is not clear.<br /> (c) The introduction of a category of non-interest that contains only 36 trials compared to 38 trials for all four categories of interest creates a design imbalance.

(4) Another imbalance is present in the design of this study: the number of trials per category is not the same in each run of the main task. This imbalance does not seem to be accounted for in the 1st-level GLM and renders a bit problematic the subsequent use of MVPA.

(5) The main claim of the authors, encapsulated by the title of the present manuscript, is not tested directly. While the authors included in their protocol independent localizers for mentalizing, language, and logic, they did not include an independent localizer for "animacy". As such, they cannot provide a within-subject evaluation of their claim, which is entirely based on the presence of a partial overlap in PC (which is also involved in a wide range of tasks) with previous results on animacy.

Reviewer #3 (Public review):

Summary:

This study employed an implicit task, showing vignettes to participants while a bold signal was acquired. The aim was to capture automatic causal inferences that emerge during language processing and comprehension. In particular, the authors compared causal inferences about illness with two control conditions, causal inferences about mechanical failures and non-causal phrases related to illnesses. All phrases that were employed described contexts with people, to avoid animacy/inanimate confound in the results. The authors had a specific hypothesis concerning the role of the precuneus (PC) in being sensitive to causal inferences about illnesses.

These findings indicate that implicit causal inferences are facilitated by semantic networks specialized for encoding causal knowledge.

Strengths:

The major strength of the study is the clever design of the stimuli (which are nicely matched for a number of features) which can tease apart the role of the type of causal inference (illness-causal or mechanical-causal) and the use of two localizers (logic/language and mentalizing) to investigate the hypothesis that the language and/or logical reasoning networks preferentially respond to causal inference regardless of the content domain being tested (illnesses or mechanical).

Weaknesses:

I have identified the following main weaknesses:

(1) Precuneus (PC) and Temporo-Parietal junction (TPJ) show very similar patterns of results, and the manuscript is mostly focused on PC (also the abstract). To what extent does the fact that PC and TPJ show similar trends affect the inferences we can derive from the results of the paper? I wonder whether additional analyses (connectivity?) would help provide information about this network.

(2) Results are mainly supported by an univariate ROI approach, and the MVPA ROI approach is performed on a subregion of one of the ROI regions (left precuneus). Results could then have a limited impact on our understanding of brain functioning.

(3) In all figures: there are no measures of dispersion of the data across participants. The reader can only see aggregated (mean) data. E.g., percentage signal changes (PSC) do not report measures of dispersion of the data, nor do we have bold maps showing the overlap of the response across participants. Only in Figure 2, we see the data of 6 selected participants out of 20.

(4) Sometimes acronyms are defined in the text after they appear for the first time.

Author response:

We thank the editors and reviewers for their comments on our manuscript. We found the comments of the reviewers helpful and plan to add new text, analyses, and figures to answer some of the outstanding questions.

In response to the reviewers’ comments, we will clarify the goal of the paper in the introduction: to test the hypothesis that causal knowledge (i.e., an intuitive theory of biology) is embedded in domain-preferring semantic networks (i.e., semantic animacy network). This work links developmental psychology work on intuitive theories and cognitive neuroscience.

As we will emphasize in the revised manuscript, the primary goal of the current paper is to test the claim that semantic networks encode causal knowledge, rather than to rule out the contribution of domain-general reasoning mechanisms to causal inference.

In response to the reviewers’ suggestions, we will add multivariate and univariate whole-cortex analyses that provide further tests for domain-general causality responses. In particular, we will include new figures showing univariate responses to the mechanical inference condition over the non-causal control conditions as well as decoding between these conditions. The reviewers have also asked us to provide individual subject dispersion data. We appreciate this suggestion, and new figures will be added to display this information.

We will also perform additional analysis in the precuneus (PC) to look for shared responses to illness and mechanical inferences. In accordance with our hypotheses, we have shown that the PC responds preferentially to illness inferences. To address the reviewers’ concerns about the selectivity of the PC to illness inferences, we will compare responses to i) illness inferences compared to the noncausal conditions and ii) mechanical inferences compared to the noncausal conditions in the PC to investigate the extent to which a shared response to causal inference across domains emerges in this region.

Critically, we find that the cortical areas that distinguish between causal and non-causal conditions in a ‘domain general manner’ (i.e., for both illness and mechanical inferences) are driven by higher responses to the non-causal condition. Moreover, these responses in prefrontal cortex and elsewhere overlap an RT predictor of neural activity, suggesting that they may reflect difficulty effects.

These results suggest that in the current task, signatures of causal inference are primarily found in domain-preferring semantic networks, rather than in domain-general fronto-parietal reasoning systems. We will provide additional discussion of the argument that the current results do not speak against the role of domain general systems across all types of causal reasoning. Instead, they suggest that the types of implicit causal inferences measured in the current study depend primarily on domain-preferring semantic networks.

The reviewers have asked us to analyze responses to causal inferences about illness in the fusiform face area (FFA). We will perform this analysis. However, we note that univariate and multivariate whole-cortex analyses that are already included in the paper did not identify lateral ventral occipito-temporal cortex as a key region involved in causal inferences about illness. Further, we do not have FFA localizer data in the current participants; therefore, the results cannot be interpreted to reflect activity in functionally defined FFA.

Two reviewers asked us to justify our choice of an implicit magic-detection task, which we will now do more clearly in the manuscript. This task was selected to ensure that participants were attending to the meaning of the vignettes. The goal of the current study was to investigate implicit causal inferences that routinely occur in language comprehension, e.g., when someone is reading a book. Past work has shown that explicitly judging the causality of causal and non-causal stimuli results in differences in response times across conditions (e.g., Kuperberg et al., 2006). In the current study, such judgments would also have introduced a confound between the behavioral decision and the condition of interest: the use of an explicit causal judgment task makes it impossible to know whether any observed neural differences between causal and non-causal conditions are simply due to differences in the selection of task responses. The selection of an orthogonal magic-detection task limits these confounds from complicating our interpretation of the neural data.

One of the reviewers asked us to justify the number of catch trials that we decided to include in our paradigm. Approximately 20% of the vignettes were “magical” vignettes (the same proportion as each of the 4 experimental conditions) to encourage participants to remain attentive throughout the task. Since these catch trials are excluded from analysis, their proportion is unlikely to influence the results of the study. We will clarify this in the manuscript.

A question was raised about the balance of trial numbers across conditions and across runs. To address this, we will include individual comparisons of each causal condition (n=36) with each non-causal condition (n=36; i.e., equal trial counts) where they are not already shown. With regard to runs, each condition is shown either 6 or 7 times per run (maximum difference of 1 trial between conditions), and the number of trials per condition is equal across the whole experiment: each condition is shown 7 times in two of the runs and 6 times four of the runs. This minor design imbalance is typical of fMRI experiments and is unlikely to impact the results. We will clarify this in the manuscript.

We believe that our planned revisions will strengthen the paper and highlight its contributions to our understanding of the neural basis of implicit causal inference.

eLife Assessment

The authors examine the effect of cell-free chromatin particles (cfChPs) derived from human serum or from dying human cells on mouse cells in culture and propose that these cfChPs can serve as vehicles for cell-to-cell active transfer of foreign genetic elements. The work presented in this paper is intriguing and potentially important, but it is incomplete. At this stage, the claim that horizontal gene transfer can occur via cfChPs would strongly benefit from additional evidence emerging from multiple independent approaches. The evolutionary interpretations associated with the concept of "predatory genome" are premature based on the strength of evidence.

Reviewer #1 (Public review):

Summary:

Horizontal gene transfer is the transmission of genetic material between organisms through ways other than reproduction. Frequent in prokaryotes, this mode of genetic exchange is scarcer in eukaryotes, especially in multicellular eukaryotes. Furthermore, the mechanisms involved in eukaryotic HGT are unknown. This article by Banerjee et al. claims that HGT occurs massively between cells of multicellular organisms. According to this study, the cell free chromatin particles (cfChPs) that are massively released by dying cells are incorporated in the nucleus of neighboring cells. These cfChPs are frequently rearranged and amplified to form concatemers, they are made of open chromatin, expressed, and capable of producing proteins. Furthermore, the study also suggests that cfChPs transmit transposable elements (TEs) between cells on a regular basis, and that these TEs can transpose, multiply, and invade receiving cells. These conclusions are based on a series of experiments consisting in releasing cfChPs isolated from various human sera into the culture medium of mouse cells, and using FISH and immunofluorescence to monitor the state and fate of cfChPs after several passages of the mouse cell line.

Strengths:

The results presented in this study are interesting because they may reveal unsuspected properties of some cell types that may be able to internalize free-circulating chromatin, leading to its chromosomal incorporation, expression, and unleashing of TEs. The authors propose that this phenomenon may have profound impacts in terms of diseases and genome evolution. They even suggest that this could occur in germ cells, leading to within-organism HGT with long-term consequences.

Weaknesses:

The claims of massive HGT between cells through internalization of cfChPs are not well supported because they are only based on evidence from one type of methodological approach: immunofluorescence and fluorescent in situ hybridization (FISH) using protein antibodies and DNA probes. Yet, such strong claims require validation by at least one, but preferably multiple, additional orthogonal approaches. This includes, for example, whole genome sequencing (to validate concatemerization, integration in receiving cells, transposition in receiving cells), RNA-seq (to validate expression), ChiP-seq (to validate chromatin state).

Another weakness of this study is that it is performed only in one receiving cell type (NIH3T3 mouse cells). Thus, rather than a general phenomenon occurring on a massive scale in every multicellular organism, it could merely reflect aberrant properties of a cell line that for some reason became permeable to exogenous cfChPs. This begs the question of the relevance of this study for living organisms.

Should HGT through internalization of circulating chromatin occur on a massive scale, as claimed in this study, and as illustrated by the many FISH foci observed in Fig 3 for example, one would expect that the level of somatic mosaicism may be so high that it would prevent assembling a contiguous genome for a given organism. Yet, telomere-to-telomere genomes have been produced for many eukaryote species, calling into question the conclusions of this study.

Reviewer #2 (Public review):

I must note that my comments pertain to the evolutionary interpretations rather than the study's technical results. The techniques appear to be appropriately applied and interpreted, but I do not feel sufficiently qualified to assess this aspect of the work in detail.

I was repeatedly puzzled by the use of the term "function." Part of the issue may stem from slightly different interpretations of this word in different fields. In my understanding, "function" should denote not just what a structure does, but what it has been selected for. In this context, where it is unclear if cfChPs have been selected for in any way, the use of this term seems questionable.

Similarly, the term "predatory genome," used in the title and throughout the paper, appears ambiguous and unjustified. At this stage, I am unconvinced that cfChPs provide any evolutionary advantage to the genome. It is entirely possible that these structures have no function whatsoever and could simply be byproducts of other processes. The findings presented in this study do not rule out this neutral hypothesis. Alternatively, some particular components of the genome could be driving the process and may have been selected to do so. This brings us to the hypothesis that cfChPs could serve as vehicles for transposable elements. While speculative, this idea seems to be compatible with the study's findings and merits further exploration.

I also found some elements of the discussion unclear and speculative, particularly the final section on the evolution of mammals. If the intention is simply to highlight the evolutionary impact of horizontal transfer of transposable elements (e.g., as a source of new mutations), this should be explicitly stated. In any case, this part of the discussion requires further clarification and justification.

In summary, this study presents important new findings on the behavior of cfChPs when introduced into a foreign cellular context. However, it overextends its evolutionary interpretations, often in an unclear and speculative manner. The concept of the "predatory genome" should be better defined and justified or removed altogether. Conversely, the suggestion that cfChPs may function at the level of transposable elements (rather than the entire genome or organism) could be given more emphasis.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

Horizontal gene transfer is the transmission of genetic material between organisms through ways other than reproduction. Frequent in prokaryotes, this mode of genetic exchange is scarcer in eukaryotes, especially in multicellular eukaryotes. Furthermore, the mechanisms involved in eukaryotic HGT are unknown. This article by Banerjee et al. claims that HGT occurs massively between cells of multicellular organisms. According to this study, the cell free chromatin particles (cfChPs) that are massively released by dying cells are incorporated in the nucleus of neighboring cells. These cfChPs are frequently rearranged and amplified to form concatemers, they are made of open chromatin, expressed, and capable of producing proteins. Furthermore, the study also suggests that cfChPs transmit transposable elements (TEs) between cells on a regular basis, and that these TEs can transpose, multiply, and invade receiving cells. These conclusions are based on a series of experiments consisting in releasing cfChPs isolated from various human sera into the culture medium of mouse cells, and using FISH and immunofluorescence to monitor the state and fate of cfChPs after several passages of the mouse cell line.

Strengths:

The results presented in this study are interesting because they may reveal unsuspected properties of some cell types that may be able to internalize free-circulating chromatin, leading to its chromosomal incorporation, expression, and unleashing of TEs. The authors propose that this phenomenon may have profound impacts in terms of diseases and genome evolution. They even suggest that this could occur in germ cells, leading to within-organism HGT with long-term consequences.

Weaknesses:

The claims of massive HGT between cells through internalization of cfChPs are not well supported because they are only based on evidence from one type of methodological approach: immunofluorescence and fluorescent in situ hybridization (FISH) using protein antibodies and DNA probes. Yet, such strong claims require validation by at least one, but preferably multiple, additional orthogonal approaches. This includes, for example, whole genome sequencing (to validate concatemerization, integration in receiving cells, transposition in receiving cells), RNA-seq (to validate expression), ChiP-seq (to validate chromatin state).

We agree with the reviewer’s suggestions. We propose to use RNA-seq using an orthogonal platform as a solution. This will allow us to answer multiple questions viz. validation of expression of human DNA in mouse cells, obtaining a detailed insight into genes and pathways driven by human cfChPs and enable us to identify chimeric human and mouse transcripts.

Another weakness of this study is that it is performed only in one receiving cell type (NIH3T3 mouse cells). Thus, rather than a general phenomenon occurring on a massive scale in every multicellular organism, it could merely reflect aberrant properties of a cell line that for some reason became permeable to exogenous cfChPs. This begs the question of the relevance of this study for living organisms.

We agree with the reviewer’s suggestion. We propose to show horizontal transfer of cfChPs using four different cell-lines representing four different species.

Should HGT through internalization of circulating chromatin occur on a massive scale, as claimed in this study, and as illustrated by the many FISH foci observed in Fig 3 for example, one would expect that the level of somatic mosaicism may be so high that it would prevent assembling a contiguous genome for a given organism. Yet, telomere-to-telomere genomes have been produced for many eukaryote species, calling into question the conclusions of this study.

The reviewer is right in expecting that the level of somatic mosaicism may be so high that it would prevent assembling a contiguous genome. This is indeed the case, and we find that beyond ~ 250 passages the genomes of the cfChPs treated NIH3T3 cells begin to die out apparently become their genomes have become too unstable for survival. This point will be highlighted in the revised version. It is likely that cell death resulting from large scale HGT creates a vicious cycle of more cell death induced by cfChPs thereby helping to explain the massive daily turnover of cells in the body (10⁹ – 10¹² cells per day).  

Reviewer #2 (Public review):

I must note that my comments pertain to the evolutionary interpretations rather than the study's technical results. The techniques appear to be appropriately applied and interpreted, but I do not feel sufficiently qualified to assess this aspect of the work in detail.

I was repeatedly puzzled by the use of the term "function." Part of the issue may stem from slightly different interpretations of this word in different fields. In my understanding, "function" should denote not just what a structure does, but what it has been selected for. In this context, where it is unclear if cfChPs have been selected for in any way, the use of this term seems questionable.

We think this is a matter of semantics. We have used the term “function” since cfChPs that enter the cell are biologically active; they transcribe, translate, synthesize, proteins and proliferate. We, therefore feel that the term function is not inappropriate.

Similarly, the term "predatory genome," used in the title and throughout the paper, appears ambiguous and unjustified. At this stage, I am unconvinced that cfChPs provide any evolutionary advantage to the genome. It is entirely possible that these structures have no function whatsoever and could simply be byproducts of other processes. The findings presented in this study do not rule out this neutral hypothesis. Alternatively, some particular components of the genome could be driving the process and may have been selected to do so. This brings us to the hypothesis that cfChPs could serve as vehicles for transposable elements. While speculative, this idea seems to be compatible with the study's findings and merits further exploration.

We take the reviewer’s point. We will replace the term “predatory genome” with a more neutral and factual term “supernumerary genome” in the title and throughout the manuscript in the revised version.

I also found some elements of the discussion unclear and speculative, particularly the final section on the evolution of mammals. If the intention is simply to highlight the evolutionary impact of horizontal transfer of transposable elements (e.g., as a source of new mutations), this should be explicitly stated. In any case, this part of the discussion requires further clarification and justification.

We propose to revise the “discussion” section taking into account the issues raised by the reviewer and highlight the potential role of cfChPs in evolution by acting as vehicles of transposable elements.  

In summary, this study presents important new findings on the behavior of cfChPs when introduced into a foreign cellular context. However, it overextends its evolutionary interpretations, often in an unclear and speculative manner. The concept of the "predatory genome" should be better defined and justified or removed altogether. Conversely, the suggestion that cfChPs may function at the level of transposable elements (rather than the entire genome or organism) could be given more emphasis.

Our responses to this paragraph are given in the two above sections.

eLife Assessment

Valencia et al. combine elegant in vitro biochemical experiments with functional assays in cardiomyocytes to determine which properties of the FHOD3 formin are essential for sarcomere assembly. Using separation-of-function mutants, they show that FHOD3's elongation activity, rather than its nucleation, capping, or bundling activities, is key to its sarcomeric function. This is an important finding; most of the data presented in the manuscript are convincing, with the exception of two experiments (presence of FHOD3 at the barbed end of actin filaments in the TIRF elongation assays and characterization of the GS-FH1 mutant phenotype) that would merit few additional controls.

Reviewer #1 (Public review):

Summary:

Formins are complex proteins with multiple effects on actin filament assembly, including nucleation, capping with processive elongation, and bundling. Determining which of these activities is important for a given biological process and normal cellular function is a major challenge.

Here, the authors study the formin FHOD3L, which is essential for normal sarcomere assembly in muscle cells. They identify point mutants of FHOD3L in which formin nucleation and elongation/bundling activities are functionally separated. Expression of these mutants in neonatal rat ventricular myocytes shows that the control of actin filament elongation by formin is the major activity required for the normal assembly of functional sarcomeres.

Strengths:

The strength of this work is to combine sensitive biochemical assays with excellent work in neonatal rat ventricular myocytes. This combination of approaches is highly effective for analyzing the function of proteins with multiple activities in vitro.

Weaknesses:

FHOD3L does not seem to be the easiest formin to study because of its relatively weak nucleation activity and the short duration of capping events. This difficulty imposes rigorous biochemical analysis and careful interpretation of the data, which should be improved in this work.

Reviewer #2 (Public review):

This article elucidates the biochemical and cellular mechanisms by which the FHOD-family of formins, particularly FHOD3, contributes to sarcomere formation and contractility in cardiomyocytes. Formins are mainly known to nucleate and elongate actin filaments, with certain family members also exhibiting capping, severing, and bundling activities. Although FHOD3 has been well-established as essential for sarcomere assembly in cardiomyocytes, its precise biochemical functions and contributions to actin dynamics remain poorly understood.

In this study, the authors combine in vitro biochemical assays with cellular experiments to dissect FHOD3's roles in actin assembly and sarcomere formation. They demonstrate that FHOD3 nucleates actin filaments and acts as a transient elongator, pausing elongation after an initial burst of filament growth. Using separation-of-function mutants, they show that FHOD3's elongation activity - rather than its nucleation, capping, or bundling capabilities - is key for its sarcomeric function.

The experiments have been conducted rigorously and well-analyzed, and the paper is clearly written. The data presented support the authors' conclusions. I appreciate the detailed description and rationale behind the FHOD3 constructs used in this study.

However, I was somewhat surprised and a bit disappointed that while the authors conducted single-color TIRF experiments to observe the effects of FHOD3 on single filaments, they did not use fluorescently labeled FHOD3 to directly visualize its behavior. Incorporating such experiments would significantly strengthen their conclusions regarding FHOD3's bursts of elongation interspersed with capping activity. While I understand this might require a few additional weeks of experiments, these data would add considerable value by directly testing the proposed mechanism.

There is a typo in the word "required" in line number 30. The authors also use fit data to extract parameters in several panels (e.g., Figures 2b, 2d, 3a, and 3b). While these fit functions may be intuitive to actin experts, explicitly describing the fit functions in the figure legends or methods would greatly benefit the broader readership.

Reviewer #3 (Public review):

Valencia et al. aim to elucidate the biochemical and cellular mechanisms through which the human formin FHOD3 drives sarcomere assembly in cardiomyocytes. To do so, they combined rigorous in vitro biochemical assays with comprehensive in vivo characterizations, evaluating two wild-type FHOD3 isoforms and two function-separating mutants. Surprisingly, they found that both wild-type FHOD3 isoforms can nucleate new actin filaments, as well as elongate existing actin filaments in conjunction with profilin following barbed-end capping. This is in addition to FHOD3's proposed role as an actin bundler. Next, the authors asked whether FHOD3L promotes sarcomere assembly in cardiomyocytes through its activity in actin nucleation or rather elongation. With two function-separating mutants, the authors evaluated the numbers and morphology of sarcomeres, as well as their ability to beat and generate cardiac rhythm. The authors found that while the wild-type FHOD3L and the K1193L mutant can rescue sarcomere morphology and physiology, the GS-FH1 mutant fails to do so. Given that in GS-FH1 mainly elongation activity is compromised, the authors concluded that the elongation activity of FHOD3 is essential for its role in sarcomere assembly in cardiomyocytes, while its nucleator activity is dispensable. Overall, this important study provided a broadened view on the biochemical activities of FHOD3, and a pioneering view on a possible cellular mechanism of how FHOD3L drives sarcomere assembly. If further validated, this can lead to new mechanistic models of sarcomere assembly and potentially new therapeutic targets of cardiomyopathy.

The conclusions of this paper are mostly well supported by the comprehensive biochemical analyses performed by the authors. However, the sarcomere assembly defect phenotype in the GS-FH1 rescue condition requires further investigation, as the extremely low level of GS-FH1 signal in transfected cells in Figure 6A may reflect a failure of actin-binding by this construct in vivo, rather than its inability to drive elongation. Though the authors do show in Figure 6 that GS-FH1 can bind to normal-looking sarcomeres when they are present, this may be due to a lack of siRNA activity in these cells, such that endogenous FHOD3L is still present. In this possible scenario, GS-FH1 may dimerize with endogenous FHOD3L. The authors should demonstrate that GS-FH1 alone can indeed interact with existing actin filaments in vivo. While this has been clearly demonstrated in vitro, given the more complex biochemical environment in vivo where additional unknown binding partners may present, cautions should be made when extrapolating findings from the former to the latter.

Author response:

We thank the reviewers for their careful readings of our paper and their very positive assessment. Here we address the two major concerns they raised, referring to the revised version of the manuscript that will be submitted:

(1) Important points were raised regarding the brief elongation events we reported. The time resolution and noise in our system reduce the accuracy of the burst velocity measurements. To address this, we have reached out to a colleague who is set up to repeat these measurements with microfluidics-assisted TIRF. The noise should be greatly reduced and the system is also optimal for directly visualizing labeled FHOD3, as suggested. We hope this experimental approach will provide new insights.

In the meantime, we analyzed our data more closely. We were asked about the pauses we observe before bursts of elongation and how we know they are functionally relevant. The short answer is that we do not know. We reported them because they were so common:  in three independent experiments with wild type FHOD3L-CT we analyzed a total of 20 filaments. We detected 112 dim regions and 97 of these were pause/burst events (~87%). Among the cases lacking a pause we include instances of apparent "double bursts" with no time for capping in between (which may be a time resolution issue) and some cases where the burst was in progress when data collection started. In the latter case, we cannot determine whether or not a pause was missed. We cannot rule out that this pause reflects an interaction with the surface but might expect the frequency to be lower if it were. In fact, we did detect pauses in the profilin-actin negative control but only 4 pauses were detected across 21 filaments analyzed compared to 97 pauses observed in the presence of wild type FHOD3L across 20 filaments analyzed. We will revise the text to make our conclusions about pauses more circumspect.

For comparison to our current data, we further analyzed the filaments in TIRF assays with no formin present. As the reviewers point out, inhomogeneities in filament intensity are normal. Thus, we examined any dim spots for pauses and/or bursts. We will report (future Figure 2G) that the velocity of growth of these dim spots was the same as the velocity of the rest of the filament. While our numbers may not be perfectly accurate due to the noise in our system, the difference of 3-4 fold increase versus no detectable change in rate is substantial and statistically different. In addition, we determined the number of dim spots per length of filament. We found a higher frequency of dim spots when FHOD3L-CT or FHOD3S-CT was present vs no formin, as will be shown in Figure 2 – figure supplement 1G and 2D.

We are convinced that the brief dim events we observed in the presence of FHOD3L-CT do, in fact, reflect formin-mediated elongation and hope that the reviewers concur. This does not preclude our interest in the microfluidics and two-color assays, which we will pursue in the future.

(2) The reviewers were concerned about the low protein levels in the GS-FH1 rescue experiments as reflected in the HA fluorescence intensity distributions shown in Fig. 5 – figure supplement 2A. While the scenario proposed could explain our observations with the GSFH1 rescues, it is quite complex and does not preclude the conclusion that the FH1 domain is critical. One limit of this scenario would be that the protein levels in the GS-FH1 cells reflect completely inactive protein, as opposed to FHOD3L that cannot elongate (by design). Given that the C-terminal half of the protein folds and functions and that the changes are made within an intrinsically disordered region, we do not favor this model. The reviewers suggest that the mutant protein detected in the few cells with (probably residual) sarcomeres could be stabilized, in part or entirely, by heterodimerization with residual endogenous wild type protein. We agree that heterodimerization is possible. The question becomes, how active is a heterodimer? If heterodimers have any activity, it seems far from sufficient to rescue sarcomere formation, suggesting that two functional FH1 domains are critical. To confirm this possibility, we would have to be able to determine whether the few sarcomeres present in these cases are residual and/or the new sarcomeres the low level of heterodimers could make. That said, we do not see evidence of correlation between protein levels and rescue at the level present in these cells (addressed below). Unfortunately, the proposed IP to test whether FHOD3L binds actin in vivo would only potentially report on filament side binding (both direct and indirect). It would not address whether the GS-FH1 mutant functions as a nucleator, elongator, bundler and/or capping protein in vivo.

If we assume that the protein present is active, the critical question that we can address is whether the phenotype is due to low protein levels or if the phenotype is due to loss of elongation activity by FHOD3L. To address this question, we revisited our data.

First, we plotted the distributions of the intensities of the cells we analyzed further, in addition to the automated readout of all the cells in the dish we originally presented (e.g. Fig. 4 – figure supplement 2A,B). These cells were selected randomly and, as should be the case, the distributions of their intensities agree well with the original distributions for the three different rescue constructs: FHOD3L, K1193L, and GS-FH1 (Fig. 6 – figure supplement 1A,B). We then asked whether there was any correlation between HA intensities with the sarcomere metrics. Consistent with in our pilot data, no correlation is evident in any of the three cases across the range of intensities we collected (400 – 2700 a.u.) (Fig. 6 – figure supplement 1C,D,E). We were originally satisfied with the GS-FH1 data, despite the low average intensity levels, because the intensities were well within the range that we established in pilot studies. These data reconfirm that the intensity levels are reasonable in a larger study.

To more specifically address the question of whether low HA fluorescence intensity is likely to reflect sufficient protein levels to build sarcomeres, we re-examined two data sets from the FHOD3L WT rescue data. We found that, by chance, the first replicate of data from the wild type rescue has a comparable intensity distribution to that of the GSFH1 rescues (580 +/- 261 / cell vs. 548 +/- 105 / cell). In addition, we collected all of the data from cells with intensity levels <720, selected to mimic the distribution of the GS-FH1 cells (Fig. 6 – figure supplement 3A). We then compared the sarcomere metrics (sarcomere number, sarcomere length, sarcomere width) between the full data set and the two low intensity subsets using statistical tests as reported for the rest of the cell biology data set:

· Sarcomere number is the only non-normal metric. We therefore used the Mann Whitney U test for each pairwise comparison, which shows no difference between all 3 WT distributions.

· We compared Z-line lengths by Student’s two-sample, unpaired t-test for each pairwise comparison, again finding no significant difference for all distributions.

· Sarcomere length shows a weakly significant difference (p=0.017 (compared to 0.033 for 3 treatment groups based on Bonferroni correction)) between the whole WT data set and bio rep 1, but no difference between the whole WT data set and the HA<720 group via Student’s two-sample, unpaired t-test.

An alternate statistical analysis approach, one-way ANOVA and Tukey post hoc tests, gave similar results. Thus, cells expressing wild type FHOD3L at levels comparable to levels detected in GS-FH1 mutant rescues, are fully rescued. Based on these findings we conclude that the expression levels in the GS-FH1 are high enough to rescue the FHOD3 knock down, supporting our conclusion that the defect is due to loss of elongation activity. We will add this analysis and discussion to the revised manuscript.

In future studies we will design less severe mutations to the FH1 domain. We hope to identify one with a strong effect on elongation and another with an intermediate effect. Once the best candidates are characterized in vitro, we will test them in our rescue experiments. If the strong mutant mimics the GS-FH1 rescue and the intermediate mutant is less severe, we will have strengthened our conclusion that elongation is a critical FHOD3L activity in sarcomere formation.

Additional improvements will be made to the manuscript based on recommendations we received from the reviewers.

eLife Assessment

This important study investigates the role of vasopressin in modulating same-sex affiliative relationships in the context of linear dominance hierarchies. It provides convincing evidence that vasopressin signaling is involved in modulating aspects of affiliative behavior, although the evidence that affiliative relationships specifically arise from the triadic interaction study design is incomplete. Nevertheless, its focus on broadening the types of social relationships and species studied in this area makes it of interest to both neuroendocrinologists and colleagues studying the evolution and mechanisms underlying social affiliation.

Reviewer #1 (Public review):

Summary:

The authors seek to establish whether triadic interaction can promote affiliative relationships in the context of strict dominance hierarchies, and whether the vasopressinergic system is involved in such affiliations. To address this, they experimentally examine how male same-sex affiliations form by testing triadic cohabitation in large-billed crows, a species where males are known to develop and maintain same-sex affiliative relationships within a strict linear social hierarchy. They show a reduction in aggressive behavior over time with cohabitation and the formation of affiliative relationships, as measured by reciprocal allopreening, between two members (dyad) of the triad. The authors then administer a V1aR antagonist to each member of the triad, finding that allopreening decreases and dominance/submissive behaviors reemerge only in the dyad that developed an affiliated relationship ("affiliated dyad") with blockade of V1aR, demonstrating that V1aR mediates maintenance of affiliative peer relationships. The questions of how peer affiliations form, particularly in the context of dominance hierarchies, and the role of V1aR in regulating these behaviors are impactful for the field of social behavior. While the experimental paradigm provides a new way of approaching these questions, we have outlined below our concerns regarding the collection and interpretation of the data that limit the impact of this particular study.

Strengths:

(1) The authors develop a behavioral paradigm and experimental sequence using large-billed crows that allows them to identify the formation of stable, affiliated dyads within triadic groups that are robust to subsequent testing and are sensitive to pharmacological manipulation.

(2) The effects of V1aR antagonism on allopreening and respective dominance or submissive behaviors appear significant and specific to the affiliated dyad, which supports the view that V1aR plays a role in context-dependent, flexible regulation of aggressive behaviors across species. However, these results are difficult to interpret with respect to the authors' main claims given the weaknesses outlined below.

Weaknesses:

(1) The authors claim that the data demonstrates that a triadic social group facilitates the formation of affiliative dyads and go further to claim that these relationships have relevance to understanding coalition formation. It is difficult to say whether the triadic structure actually facilitates or promotes the formation of these affiliative interactions as stated without direct comparisons to alternately sized groupings. Further, the relevance to coalitions is weak without expanded behavioral testing.

(2) Aspects of the experimental design introduce confounding factors that make it difficult to interpret the resulting data. In experiment 1, 6 of the 18 animals that are used for testing are part of multiple triads. This is not accounted for in either the experimental design (wash-out period prior to reuse of animals) or statistical analysis (including repeated testing as a factor in the model) or is not described. Further, while the authors do randomize and counterbalance the two dose trials for the antagonist, vehicle vs drug exposure is not randomized.

(3) The re-emergence of dominance-related agonistic behaviors with V1aR antagonism specifically in the affiliated dyads is interesting, but difficult to interpret without further description and analysis of the dyadic behavior, particularly given the absence of dominance-related behaviors in either affiliated or unaffiliated dyads during the cohabitation period. In addition, the current data does not support the hypothesis that V1aR is also required to form affiliative relationships, as stated in the discussion (Lines 464-5, 472, 494), since the authors did not administer V1aR antagonist during the initial period of triadic cohabitation.

(4) Sentences are often repetitive or duplicated (lines 424-426), and paragraphs should be condensed for easier reading, especially in the discussion. Further, some of the discussion might be better presented in an "Ideas and Speculation" subsection, which would help readers appropriately assess the validity of the conclusions based on the data vs the larger implications suggested by the authors.

Reviewer #2 (Public review):

Seguchi and Izawa provide robust evidence for the role of vasopressin in modulating same-sex affiliative relationships. Especially striking is that these effects appear to be selective to key relationships within a triadic social context. Overall, this is an interesting and rich dataset with compelling results. I largely have some clarifying questions.

Experiment 1 Comments:

(1) The primary argument/finding in this experiment is that a triadic situation/environment facilitates the development of male-male reciprocal social relationships. Overall, this effect appears striking in that male-male affiliative bonds (defined as reciprocal allopreening) formed in 6 of the 8 triads tested. However, there is no comparison group of dyads to determine whether co-housing for 2 weeks could also support the formation of male-male social bonds. This lack of a comparison group makes it unclear to what extent the triad is the key aspect of the environment that supports social bonding.

(2) More specifically, the authors argue that it is not just that triads support affiliative male-male bonds, but that bonds form between the second "middle" (dominant/subordinate) and third "low" (subordinate/subordinate) individuals in each triad. However, it was difficult to assess this from the results.<br /> a) For example, in Figure 3B is each data point the average of two individuals, since in each triad there are two dominant and two subordinate individuals?<br /> b) For me, using more precise language beyond dominant and subordinate (e.g. middle and low), and more clearly displaying the results of allopreening for each pairwise dyad within a triad would improve the impact of the results and support the authors' argument.

(3) Experiment 2 Comments:<br /> The results here are quite striking, despite the low sample size. In Figure 4, it appears that in every instance of administration V1aRA low and high administration decreased allopreening for both dominant and subordinate individuals.

(4) Some methodological questions:<br /> a) Can you clarify whether the duration of the post-test was also 30 min?<br /> b) As in Experiment 1, how are individual birds represented in the triad? Was the second "Middle" bird (dominant/subordinate) tested as both a dominant and subordinate bird? My understanding is that the dominant and subordinate birds in Figure 4 are different individuals but that they are the same individuals represented between the affiliated dyad and unaffiliated dyad.

(5) Throughout the manuscript (Lines 57-67; 557-566) the authors argue that the role of VP in regulating gregariousness can be extrapolated to understand the role of same-sex affiliative bonding. Importantly, gregariousness does not necessarily reflect affiliative bonding. While allopreening is specifically associated with social bonding (e.g. monogamous pair bonds) independent of broader social systems, gregariousness in general, and specifically as defined in many of the references cited, is independent of social bonds - in fact, it is assessed primarily in novel social contexts.

(6) To clarify, adult prairie voles in the wild do not engage in same-sex affiliative behavior commonly. In fact one of the primary components of opposite-sex pair bonding is same-sex aggression. Thus, while mechanistic studies on the neurobiology of same-sex peer bonds are relevant for this work, I am less convinced that you can make comparisons between the ultimate function of same-sex affiliative relationships in prairie voles.

(17) The results here are consistent with VP having an anxiolytic effect, as has been suggested in birds, with the consequences on social behaviors being directly or indirectly related. This may be a useful point to draw on in the discussion when considering your findings.

Reviewer #3 (Public review):

Summary:

In this study, Seguchi & Izawa investigate the formation of male-male affiliative relationships within triads of large-billed crows. They then administered a vasopressin 1a receptor (V1aR) antagonist to either the dominant or subordinate individual within affiliative dyads, to examine whether blocking V1aR disrupts affiliative behavior. They discovered that affiliative dyads can be induced in large-billed crows by housing them in triads. They also found that blocking V1aRs significantly decreased allopreening (an affiliative behavior) within dyads. In addition, it increased aggression by dominant individuals and submissive calls by subordinate individuals.

Strengths:

This manuscript uses an especially interesting species - a highly intelligent and highly social corvid, with complex dominance hierarchies - to extend previous work into the effects of the oxytocin and vasopressin peptides hormones on social behaviors. The results are surprisingly clear, despite a small sample size. The authors use the correct statistical approaches to account for a complex, nested design. The introduction and discussion both reflect a strong understanding of the relevant literature, including the limitations of extrapolating from peripheral (intramuscular) versus central (into the brain) injections of the V1aR antagonist. In addition, the authors appear to have been transparent about the data and results, accounting for some of the challenges and limitations of the data and study.

Weaknesses:

There are two major concerns. First, the study has a very low sample size (8 triads for Experiment 1, and only 5 triads for Experiment 2). Despite the surprisingly convincing findings, the sample size is too small to support the claim that the vasopressin system "universally mediates same sex relationships. Secondly, the study does not account for the effects of V1aR on non-social behaviors. This is especially true because vasopressin/V1aR (and the particular antagonist used in this study) is known to have effects on osmotic balance, food intake, and stress, including in birds. My concern is that the behavioral effects could be accounted before by differences in general stress or activity levels. Allopreening is usually an activity performed in periods of relative inactivity with aggression being more characterized by high activity levels. The authors discuss these different effects of vasopressin/V1aR in the Discussion, but they do not account for these effects in the study design.

eLife Assessment

In their valuable study, Lee et al. explore a role for the Hippo signaling pathway, specifically wts-1/LATS and the downstream regulator yap, in age-dependent neurodegeneration and microtubule dynamics using C. elegans mechanosensory neurons as a model. The authors demonstrate that disruption of wts-1/LATS leads to age-associated morphological and functional neuronal abnormalities, linked to enhanced microtubule stabilization, and shows a genetic connection between yap and microtubule stability. Despite some mechanistic gaps, the study employs robust genetic and molecular approaches to reveal a convincing link between the Hippo pathway, microtubule dynamics, and neurodegeneration.

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors investigate the role of microtubule dynamics and its effects on neuronal aging. Using C. elegans as a model, the authors investigate the role of evolutionarily conserved Hippo pathway in microtubule dynamics of touch receptor neurons (TRNs) in an age-dependent manner. Using genetic, molecular, behavioral, and pharmacological approaches, the authors show that age-dependent loss of microtubule dynamics might underlie structural and functional aging of TRNs. Further, the authors show that the Hippo pathway specifically functions in these neurons to regulate microtubule dynamics. Specifically, authors show that hyperactivation of YAP-1, a downstream component of the Hippo pathway that is usually inhibited by the kinase activity of the upstream components of the pathway, results in microtubule stabilization and that might underlie the structural and functional decline of TRNs with age. However, how the Hippo pathway regulates microtubule dynamics and neuronal aging was not investigated by the authors.

Strengths:

This is a well-conducted and well-controlled study, and the authors have used multiple approaches to address different questions.

Weaknesses:

There are no major weaknesses identified, except that the effect of the Hippo pathway seems to be specific to only a subset of neurons. I would like the authors to address the specificity of the effect of the Hippo pathway in TRNs, in their resubmission.

Reviewer #2 (Public review):

Summary:

This study examines a novel role of the Hpo signaling pathway, specifically of wts-1/LATS and the downstream regulator of gene expression, yap, in age-related neurodegeneration in C. elegans touch-responsive mechanosensory neurons, ALM and PLM. The study shows that knockdown or deletion of wts-1/LATS causes age-associated morphological abnormalities of these neurons, accompanied by functional loss of touch responsiveness. This is further associated with enhanced, abnormal, microtubule stabilization in these neurons.

Strengths:

This study examines a novel role of the Hpo signaling pathway, specifically of wts-1/LATS and the downstream regulator of gene expression, yap, in age-related neurodegeneration in C. elegans touch-responsive mechanosensory neurons, ALM and PLM. The study shows that knockdown or deletion of wts-1/LATS causes age-associated morphological abnormalities of these neurons, accompanied by functional loss of touch responsiveness. This is further associated with enhanced, abnormal, microtubule stabilization in these neurons. Strong pharmacological and especially genetic manipulations of MT-stabilizing or severing proteins show a strong genetic link between yap and regulation of MTs stability. The study is strong and uses robust approaches, especially strong genetics. The demonstrations on the aging-related roles of the Hpo signaling pathway, and the link to MTs, are novel and compelling. Nevertheless, the study also has mechanistic weaknesses (see below).

Weaknesses:

Specific comments:

(1) The study demonstrates age-specific roles of the Hpo pathway, specifically of wts-1/LATS and yap, specifically in TRN mechanosensory neurons, without observing developmental defects in these neurons, or effects in other neurons. This is a strong demonstration. Nevertheless, the study does not address whether there is a correlation of Hpo signaling pathway activity decline specifically in these neurons, and not other neurons, and at the observed L4 stage and onwards (including the first day of adulthood, 1DA stage). Such demonstrations of spatio-temporal regulation of the Hpo signaling pathway and its activation seem important for linking the Hpo pathway with the observed age-related neurodegeneration. Can this age-related response be correlated to indeed a decline in Hpo signaling during adulthood? Especially at L4 and onwards? It will be informative to measure this by examining the decline in wts1 as well as yap levels and yap nuclear localization.

(2) The Hpo pathway eventually activates gene expression via yap. Although the study uses robust genetic manipulations of yap and wts-1/LATS, it is not clear whether the observed effects are attributed to yap-mediated regulation of gene expression (see 3).

(3) The observations on the abnormal MT stabilization, and the subsequent genetic examinations of MT-stability/severing genes, are a significant strength of the study. Nevertheless, despite the strong genetic links to yap and wts-1/LATS, it is not clear whether MT-regulatory genes are regulated by transcription downstream of the Hpo pathway, thus not enabling a strong causal link between MT regulation and Hpo-mediated gene expression, making this strong part of the study mechanistically circumstantial. Specifically, it will be good to examine whether the genes addressed herein, for example, Spastin, are transcriptionally regulated downstream of the Hpo pathway. This comment is augmented by the finding that in the wts-1/ yap-1 double mutants, MT abnormality, and subsequent neuronal morphology and touch responses are restored, clearly indicating that there is an associated transcriptional regulation

Other comments:

(1) The TRN-specific knockdown of wts-1 and yap-1 is a clear strength. Nevertheless, these do not necessarily show cell-autonomous effects, as the yap transcription factor may regulate the expression of external cues, secreted or otherwise, thus generating non-cell autonomous effects. For example, it is known that yap regulates TGF-beat expression and signaling.

(2) Continuing from comment (3) above, it seems that many of the MT-regulators chosen here for genetic examinations were chosen based on demonstrated roles in neurodegeneration in other studies. It would be good to show whether these MT-associated genes are directly regulated by transcription by the Hpo pathway.

(3) The impairment of the touch response may not be robust: it is only a 30-40% reduction at L4, and even less reduction at 1DA. It would be good to offer possible explanations for this finding.

eLife Assessment

This important work advances our understanding of parabrachial CGRP threat function. The evidence supporting CGRP aversive outcome signaling is solid, while the evidence for cue signaling and fear behavior generation is incomplete. The work will be of interest to neuroscientists studying defensive behaviors.

Reviewer #1 (Public Review):

Summary

The authors asked if parabrachial CGRP neurons were only necessary for a threat alarm to promote freezing or were necessary for a threat alarm to promote a wider range of defensive behaviors, most prominently flight.

Major Strengths of Methods and Results

The authors performed careful single-unit recording and applied rigorous methodologies to optogenetically tag CGRP neurons within the PBN. Careful analyses show that single-units and the wider CGRP neuron population increases firing to a range of unconditioned stimuli. The optogenetic stimulation of experiment 2 was comparatively simpler but achieved its aim of determining the consequence of activating CGRP neurons in the absence of other stimuli. Experiment 3 used a very clever behavioral approach to reveal a setting in which both cue-evoked freezing and flight could be observed. This was done by having the unconditioned stimulus be a "robot" traveling along a circular path at a given speed. Subsequent cue presentation elicited mild flight in controls and optogenetic activation of CGRP neurons significantly boosted this flight response. This demonstrated for the first time that CGRP neuron activation does more than promote freezing. The authors conclude by demonstrating that bidirectional modulation of CGRP neuron activity bidirectionally affects freezing in a traditional fear conditioning setting and affects both freezing and flight in a setting in which the robot served as the unconditioned stimulus. Altogether, this is a very strong set of experiments that greatly expand the role of parabrachial CGRP neurons in threat alarm.

Weaknesses

In all of their conditioning studies the authors did not include a control cue. For example, a sound presented the same number of times but unrelated to US (shock or robot) presentation. This does not detract from their behavioral findings. However, it means the authors do not know if the observed behavior is a consequence of pairing. Or is a behavior that would be observed to any cue played in the setting? This is particularly important for the experiments using the robot US.

The authors make claims about the contribution of CGRP neurons to freezing and fleeing behavior, however, all of the optogenetic manipulations are centered on the US presentation period. Presently, the experiments show a role for these neurons in processing aversive outcomes but show little role for these neurons in cue responding or behavior organizing. Claims of contributions to behavior should be substantiated by manipulations targeting the cue period.

Appraisal

The authors achieved their aims and have revealed a much greater role for parabrachial CGRP neurons in threat alarm.

Discussion

Understanding neural circuits for threat requires us (as a field) to examine diverse threat settings and behavioral outcomes. A commendable and rigorous aspect of this manuscript was the authors decision to use a new behavioral paradigm and measure multiple behavioral outcomes. Indeed, this manuscript would not have been nearly as impactful had they not done that. This novel behavior was combined with excellent recording and optogenetic manipulations - a standard the field should aspire to. Studies like this are the only way that we as a field will map complete neural circuits for threat.

Reviewer #2 (Public Review):

-Summary of the Authors' Aims:<br /> The authors aimed to investigate the role of calcitonin gene-related peptide (CGRP) neurons in the parabrachial nucleus (PBN) in modulating defensive behaviors in response to threats. They sought to determine whether these neurons, previously shown to be involved in passive freezing behavior, also play a role in active defensive behaviors, such as fleeing, when faced with imminent threats.

-Major Strengths and Weaknesses of the Methods and Results:<br /> The authors utilized an innovative approach by employing a predator-like robot to create a naturalistic threat scenario. This method allowed for a detailed observation of both passive and active defensive behaviors in mice. The combination of electrophysiology, optogenetics, and behavioral analysis provided a comprehensive examination of CGRP neuron activity and its influence on defensive behaviors. The study's strengths lie in its robust methodology, clear results, and the multi-faceted approach that enhances the validity of the findings.

No notable weakness found.

-Appraisal of Aims and Results:<br /> The authors successfully achieved their aims by demonstrating that CGRP neurons in the PBN modulate both passive and active defensive behaviors. The results clearly show that activation of these neurons enhances fear memory and promotes conditioned fleeing behavior, while inhibition reduces these responses. The study provides strong evidence supporting the hypothesis that CGRP neurons act as a comprehensive alarm system in the brain.

-Impact on the Field and Utility of Methods and Data:<br /> This work has significant implications for the field of neuroscience, particularly in understanding the neural mechanisms underlying adaptive defensive behaviors. The innovative use of a predator-like robot to simulate naturalistic threats adds ecological validity to the findings and may inspire future studies to adopt similar approaches. The comprehensive analysis of CGRP neuron activity and its role in defensive behaviors provides valuable data that could be useful for researchers studying fear conditioning, neural circuitry, and behavior modulation.

-Additional Context:<br /> The study builds on previous research that primarily focused on the role of CGRP neurons in passive defensive responses, such as freezing. By extending this research to include active responses, the authors have provided a more complete picture of the role of these neurons in threat detection and response. The findings highlight the versatility of CGRP neurons in modulating different types of defensive behaviors based on the perceived intensity and immediacy of threats.

Overall, this manuscript makes a significant contribution to our understanding of the neural basis of defensive behaviors and offers valuable methodological insights for future research in the field.

Reviewer #3 (Public Review):

Strengths:<br /> The study used optogenetics together with in vivo electrophysiology to monitor CGRP neuron activity in response to various aversive stimuli including robot chasing to determine whether they encode noxious stimuli differentially. The study used an interesting conditioning paradigm to investigate the role of CGRP neurons in the PBN in both freezing and flight behaviors.

Weakness:<br /> The major weakness of this study is that the chasing robot threat conditioning model elicits weak unconditioned and conditioned flight responses, making it difficult to interpret the robustness of the findings. Furthermore, the conclusion that the CGRP neurons are capable of inducing flight is not substantiated by the data. No manipulations are made to influence the flight behavior of the mouse. Instead, the manipulations are designed to alter the intensity of the unconditioned stimulus.

eLife Assessment

This study presents a valuable advancement in antiviral research by applying SHAPE-Map to analyze the secondary structure of the Porcine Epidemic Diarrhoea Virus RNA genome in infected cells, identifying promising therapeutic targets within viral genomic RNA. The authors provide convincing evidence of potential antiviral targetable RNA regions through a wide array of data from different methods, supported by well-documented experimental design and data analysis, demonstrating how RNA structural probing can effectively discover RNA targets and enabling further discoveries in the field. The work will be of interest to researchers focused on RNA therapeutics and viral genome studies.

Reviewer #1 (Public review):

Summary:

This study investigates the potential of targeting specific regions within the RNA genome of the Porcine Epidemic Diarrhea Virus (PEDV) for antiviral drug development. The authors used SHAPE-MaP to analyze the structure of the PEDV RNA genome in infected cells. They categorized different regions of the genome based on their structural characteristics, focusing on those that might be good targets for drugs or small interfering RNAs (siRNAs).

They found that dynamic single-stranded regions can be stabilized by compounds (e.g., to form G-quadruplexes), which inhibit viral proliferation. They demonstrated this by targeting a specific G4-forming sequence with a compound called Braco-19. The authors also describe stable (structured) single-stranded regions that they used to design siRNAs showing that they effectively inhibited viral replication.

Strengths:

There are a number of strengths to highlight in this manuscript.

(1) The study uses a sophisticated technique (SHAPE-MaP) to analyze the PEDV RNA genome in situ, providing valuable insights into its structural features.

(2) The authors provide a strong rationale for targeting specific RNA structures for antiviral development.

(3) The study includes a range of experiments, including structural analysis, compound screening, siRNA design, and viral proliferation assays, to support their conclusions.

(4) Finally, the findings have potential implications for the development of new antiviral therapies against PEDV and other RNA viruses.

Overall, this interesting study highlights the importance of considering RNA structure when designing antiviral therapies and provides a compelling strategy for identifying promising RNA targets in viral genomes.

Weaknesses:

I have some concerns about the utility of the 3D analyses, the effects of their synonymous mutants on expression/proliferation, a potentially missed control for studies of mutants, and the therapeutic utility of the compound they tested vs. G-quadruplexes.

Reviewer #2 (Public review):

Summary:

Luo et. al. use SHAPE-MaP to find suitable RNA targets in Porcine Epidemic Diarrhoea Virus. Results show that dynamic and transient structures are good targets for small molecules, and that exposed strand regions are adequate targets for siRNA. This work is important to segment the RNA targeting.

Strengths:

This work is well done and the data supports its findings and conclusions. When possible, more than one technique was used to confirm some of the findings.

Weaknesses:

The study uses a cell line that is not porcine (not the natural target of the virus).

Reviewer #3 (Public review):

Summary:

This manuscript by Luo et al. applied SHAPE-Map to analyze the secondary structure of the Porcine Epidemic Diarrhoea Virus (PEDV) RNA genome in infected cells. By combining SHAPE reactivity and Shannon entropy, the study indicated that the folding of the PEDV genomic RNA was nonuniform, with the 5' and 3' untranslated regions being more compactly structured, which revealed potentially antiviral targetable RNA regions. Interestingly, the study also suggested that compounds bound to well-folded RNA structures in vitro did not necessarily exhibit antiviral activity in cells, because the binding of these compounds did not necessarily alter the functions of the well-folded RNA regions. Later in the manuscript, the authors focus on guanine-rich regions, which may form G-quadruplexes and be potential targets for small interfering RNA (siRNA). The manuscript shows the binding effect of Braco-19 (a G-quadruplex-binding ligand) to a predicted G4 region in vitro, along with the inhibition of PEDV proliferation in cells. This suggests that targeting high SHAPE-high Shannon G4 regions could be a promising approach against RNA viruses. Lastly, the manuscript identifies 73 single-stranded regions with high SHAPE and low Shannon entropy, which demonstrated high success in antiviral siRNA targeting.

Strengths:

The paper presents valuable data for the community. Additionally, the experimental design and data analysis are well documented.

Weakness:

The manuscript presents the effect of Braco-19 on PQS1, a single G4 region with high SHAPE and high Shannon entropy, to suggest that "the compound can selectively target the PQS1 of the high SHAPE-high Shannon region in cells" (lines 625-626). While the effect of Braco-19 on PQS1 is supported by strong evidence in the manuscript, the conclusion regarding the G4 region with high SHAPE and high Shannon entropy is based on a single target, PQS1.

Author response:

The following is the authors’ response to the current reviews.

Reviewer #1 (Public review):

Summary:

In the manuscript "Intergenerational transport of double-stranded RNA limits heritable epigenetic changes," Shugarts and colleagues investigate intergenerational dsRNA transport in the nematode C. elegans. By inducing oxidative damage, they block dsRNA import into cells, which affects heritable gene regulation in the adult germline (Fig. 2). They identify a novel gene, sid-1-dependent gene-1 (sdg-1), upregulated upon SID-1 inhibition (Fig. 3). Both transient and genetic depletion of SID-1 lead to the upregulation of sdg-1 and a second gene, sdg-2 (Fig. 5). Interestingly, while sdg-1 expression suggests a potential role in dsRNA transport, neither its overexpression nor loss-of-function impacts dsRNA-mediated silencing in the germline (Fig. 7).

Strengths:

• The authors employ a robust neuronal stress model to systematically explore SID-1 dependent intergenerational dsRNA transport in C. elegans.

• They discover two novel SID-1-dependent genes, sdg-1 and sdg-2.

• The manuscript is well-written and addresses the compelling topic of dsRNA signaling in C. elegans.

Weaknesses:

• The molecular mechanism downstream of SDG-1 remains unclear. Testing whether sdg-2 functions redundantly with sdg-1could provide further insights.

• SDG-1 dependent genes in other nematodes remain unknown.

We thank the reviewer for highlighting the strengths of the work along with a couple of the interesting future directions inspired by the reported discoveries. The restricted presence of genes encoding SDG-1 and its paralogs within retrotransposons suggests intriguing evolutionary roles for these proteins. Future work could examine whether such fast-evolving or newly evolved proteins with potential roles in RNA regulation are more broadly associated with retrotransposons. Multiple SID-1-dependent proteins (including SDG-1 and SDG-2) could act together to mediate downstream effects. This possibility can be tested using combinatorial knockouts and overexpression strains. Both future directions have the potential to illuminate the evolutionarily selected roles of dsRNA-mediated signaling through SID-1, which remain a mystery.

Reviewer #2 (Public review):

Summary:

RNAs can function across cell borders and animal generations as sources of epigenetic information for development and immunity. The specific mechanistic pathways how RNA travels between cells and progeny remains an open question. Here, Shugarts, et al. use molecular genetics, imaging, and genomics methods to dissect specific RNA transport and regulatory pathways in the C. elegans model system. Larvae ingesting double-stranded RNA is noted to not cause continuous gene silencing throughout adulthood. Damage of neuronal cells expressing double-stranded target RNA is observed to repress target gene expression in the germline. Exogenous short or long double-stranded RNA required different genes for entry into progeny. It was observed that the SID-1 double-stranded RNA transporter showed different expression over animal development. Removal of the sid-1 gene caused upregulation of two genes, the newly described sid-1-dependent gene sdg-1 and sdg-2. Both genes were observed to be negatively regulated by other small RNA regulatory pathways. Strikingly, loss then gain of sid-1 through breeding still caused variability of sdg-1 expression for many, many generations. SDG-2 protein co-localizes with germ granules, intracellular sites for heritable RNA silencing machinery. Collectively, sdg-1 presents a model to study how extracellular RNAs can buffer gene expression in germ cells and other tissues.

Strengths:

(1) Very cleaver molecular genetic methods and genomic analyses, paired with thorough genetics, were employed to discover insights into RNA transport, sdg-1 and sdg-2 as sid-1-dependent genes, and sdg-1's molecular phenotype.

(2) The manuscript is well cited, and figures reasonably designed.

(3) The discovery of the sdg genes being responsive to the extracellular RNA cell import machinery provides a model to study how exogenous somatic RNA is used to regulate gene expression in progeny. The discovery of genes within retrotransposons stimulates tantalizing models how regulatory loops may actually permit the genetic survival of harmful elements.

Weaknesses:

(1) The manuscript is broad, making it challenging to read and consider the data presented. Of note, since the original submission, the authors have improved the clarity of the writing and presentation.

Comments on revised version:

This reviewer thanks the authors for their efforts in revising the manuscript. In their rebuttal, the authors acknowledged the broad scope of their manuscript. I concur. While I still think the manuscript is a challenge to read due to its expansive nature, the current draft is substantially improved when compared to the previous one. This work will contribute to our general knowledge of RNA biology, small RNA regulatory pathways, and RNA inheritance.

We thank the reviewer for highlighting the strengths of the manuscript and for helping us improve the presentation of our results and discussion.

---

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public review):

Summary:

In the manuscript "Intergenerational transport of double-stranded RNA limits heritable epigenetic changes" Shugarts and colleagues investigate intergenerational dsRNA transport in the nematode C. elegans. They induce oxidative damage in worms, blocking dsRNA import into cells (and potentially affecting the worms in other ways). Oxidative stress inhibits dsRNA import and the associated heritable regulation of gene expression in the adult germline (Fig. 2). The authors identify a novel gene, sid-1-dependent gene-1 (sdg-1), which is induced upon inhibition of SID-1 (Fig. 3). Both transient inhibition and genetic depletion of SID-1 lead to the upregulation of sdg-1 and a second gene, sdg-2 (Fig. 5). The expression of SDG-1 is variable, potentially indicating buffering regulation. While the expression of Sdg-1 could be consistent with a role in intergenerational transport of dsRNA, neither its overexpression nor loss-of-function impacts dsRNA-mediated silencing (Fig. 7) in the germline. It would be interesting to test if sdg-2 functions redundantly.

In summary, the authors have identified a novel worm-specific protein (sdg-1) that is induced upon loss of dsRNA import via SID-1, but is not required to mediate SID-1 RNA regulatory effects.

We thank the reviewer for highlighting our findings on SDG-1. We found that oxidative damage in neurons enhanced dsRNA transport into the germline and/or subsequent silencing.

Remaining Questions:

• The authors use an experimental system that induces oxidative damage specifically in neurons to release dsRNAs into the circulation. Would the same effect be observed if oxidative damage were induced in other cell types?

It is possible that oxidative damage of other tissues using miniSOG (as demonstrated in Xu and Chisholm, 2016) could also enhance the release of dsRNA into the circulation from those tissues. However, future experiments would be needed to test this empirically because it is also possible that the release of dsRNA depends on physiological properties (e.g., the molecular machinery promoting specific secretion) that are particularly active in neurons. We chose to use neurons as the source of dsRNA because by expressing dsRNA in a variety of tissues, neurons appeared to be the most efficient at the export of dsRNA as measured using SID-1-dependent silencing in other tissues (Jose et al., PNAS, 2009).

• Besides dsRNA, which other RNAs and cellular products (macromolecules and small signalling molecules) are released into the circulation that could affect the observed changes in germ cells?

We do not yet know all the factors that could be released either in naive animals or upon oxidative damage of neurons that influence the uptake of dsRNA into other tissues. The dependence on SID-1 for the observed enhancement of silencing (Fig. 2) shows that dsRNA is necessary for silencing within the germline. Whether this import of dsRNA occurs in conjunction with other factors (e.g., the uptake of short dsRNA along with yolk into oocytes (Marré et al., PNAS, 2016)) before silencing within the germline will require further study. A possible approach could be the isolation of extracellular fluid (Banse and Hunter, J Vis Exp., 2012) followed by characterization of its contents. However, the limited material available using this approach and the difficulty in avoiding contamination from cellular damage by the needle used for isolating the material make it challenging.

• SID-1 modifies RNA regulation within the germline (Fig. 7) and upregulates sdg-1 and sdg-2 (Fig. 5). However, SID-1's effects do not appear to be mediated via sdg-1. Testing the role of sdg-2 would be intriguing.

We observe the accumulation of sdg-1 and sdg-2 RNA in two different mutants lacking SID-1, which led us to conservatively focus on the analysis of one of these proteins for this initial paper. We expect that more sensitive analyses of the RNA-seq data will likely reveal additional genes regulated by SID-1. With the ability to perform multiplexed genome-editing, we hope in future work to generate strains that have mutations in many SID-1-dependent genes to recapitulate the defects observed in sid-1(-) animals. Indeed, as surmised by the reviewer, we are focusing on sdg-2 as the first such SID-1-dependent gene to analyze using mutant combinations.

• Are sdg-1 or sdg-2 conserved in other nematodes or potentially in other species?  appears to be encoded or captured by a retro-element in the C. elegans genome and exhibits stochastic expression in different isolates. Is this a recent adaptation in the C. elegans genome, or is it present in other nematodes? Does loss-of-function of sdg-1 or sdg-2 have any observable effect?

Clear homologs of SDG-1 and SDG-2 are not detectable outside of C. elegans. Consistent with the location of the sdg-1 gene within a Cer9 retrotransposon that appears to have integrated only within the C. elegans genome, sequence conservation between the genomes of related species is only observed outside the region of the retrotransposon (see Author response image 1, screenshot from UCSC browser). There were no obvious defects detected in animals lacking sdg-1 (Fig. 7) or in animals lacking sdg-2 (data not shown). It is possible that further exploration of both mutants and mutant combinations lacking additional SID-1-dependent genes would reveal defects. We also plan to examine these mutants in sensitized genetic backgrounds where one or more members of the RNA silencing pathway have been compromised.

Author response image 1.

Clarification for Readability:

To enhance readability and avoid misunderstandings, it is crucial to specify the model organism and its specific dsRNA pathways that are not conserved in vertebrates:

We agree with the reviewer and thank the reviewer for the specific suggestions provided below. To take the spirit of the suggestion to heart we have instead changed the title of our paper to clearly signal that the entire study only uses C. elegans. We have titled the study ‘Intergenerational transport of double-stranded RNA in C. elegans can limit heritable epigenetic changes’

• In the first sentence of the paragraph "Here, we dissect the intergenerational transport of extracellular dsRNA ...", the authors should specify "in the nematode C. elegans". Unlike vertebrates, which recognise dsRNA as a foreign threat, worms and other invertebrates pervasively use dsRNA for signalling. Additionally, worms, unlike vertebrates and insects, encode RNA-dependent RNA polymerases that generate dsRNA from ssRNA substrates, enabling amplification of small RNA production. Especially in dsRNA biology, specifying the model organism is essential to avoid confusion about potential effects in humans.

We agree with most statements made by the reviewer, although whether dsRNA is exclusively recognized as a foreign threat by all vertebrates of all stages remains controversial. Our changed title now eliminates all ambiguity regarding the organism used in the study.

• Similarly, the authors should specify "in C. elegans" in the sentence "Therefore, we propose that the import of extracellular dsRNA into the germline tunes intracellular pathways that cause heritable RNA silencing." This is important because C. elegans small RNA pathways differ significantly from those in other organisms, particularly in the PIWI-interacting RNA (piRNA) pathways, which depend on dsRNA in C. elegans but uses ssRNA in vertebrates. Specification is crucial to prevent misinterpretation by the reader. It is well understood that mechanisms of transgenerational inheritance that operate in nematodes or plants are not conserved in mammals.

The piRNAs of C. elegans are single-stranded but are encoded by numerous independent genes throughout the genome. The molecules used for transgenerational inheritance of epigenetic changes that have been identified thus far are indeed different in different organisms. However, the regulatory principles required for transgenerational inheritance are general (Jose, eLife, 2024). Nevertheless, we have modified the title to clearly state that the entire study is using C. elegans.  

• The first sentence of the discussion, "Our analyses suggest a model for ...", would also benefit from specifying "in C. elegans". The same applies to the figure captions. Clarification of the model organism should be added to the first sentence, especially in Figure 1.

With the clarification of the organism used in the title, we expect that all readers will be able to unambiguously interpret our results and the contexts where they apply. 

Reviewer #2 (Public review):

Summary:

RNAs can function across cell borders and animal generations as sources of epigenetic information for development and immunity. The specific mechanistic pathways how RNA travels between cells and progeny remains an open question. Here, Shugarts, et al. use molecular genetics, imaging, and genomics methods to dissect specific RNA transport and regulatory pathways in the C. elegans model system. Larvae ingesting double stranded RNA is noted to not cause continuous gene silencing throughout adulthood. Damage of neuronal cells expressing double stranded target RNA is observed to repress target gene expression in the germline. Exogenous supply of short or long double stranded RNA required different genes for entry into progeny. It was observed that the SID-1 double-stranded RNA transporter showed different expression over animal development. Removal of the sid-1 gene caused upregulation of two genes, the newly described sid-1-dependent gene sdg-1 and sdg-2. Both genes were observed to also be negatively regulated by other small RNA regulatory pathways. Strikingly, loss then gain of sid-1 through breeding still caused variability of sdg-1 expression for many, many generations. SDG-2 protein co-localizes with a Z-granule marker, an intracellular site for heritable RNA silencing machinery. Collectively, sdg-1 presents a model to study how extracellular RNAs can buffer gene expression in germ cells and other tissues.

We thank the reviewer for highlighting our findings and underscoring the striking nature of the discovery that mutating sid-1 using genome-editing resulted in a transgenerational change that could not be reversed by changing the sid-1 sequence back to wild-type.

Strengths:

(1) Very clever molecular genetic methods and genomic analyses, paired with thorough genetics, were employed to discover insights into RNA transport, sdg-1 and sdg-2 as sid-1-dependent genes, and sdg-1's molecular phenotype.

(2) The manuscript is well cited, and figures reasonably designed.

(3) The discovery of the sdg genes being responsive to the extracellular RNA cell import machinery provides a model to study how exogenous somatic RNA is used to regulate gene expression in progeny. The discovery of genes within retrotransposons stimulates tantalizing models how regulatory loops may actually permit the genetic survival of harmful elements.

We thank the reviewer for the positive comments.

Weaknesses:

(1) As presented, the manuscript is incredibly broad, making it challenging to read and consider the data presented. This concern is exemplified in the model figure, that requires two diagrams to summarize the claims made by the manuscript.

RNA interference (RNAi) by dsRNA is an organismal response where the delivery of dsRNA into the cytosol of some cell precedes the processing and ultimate silencing of the target gene within that cell. These two major steps are often not separately considered when explaining observations. Yet, the interpretation of every RNAi experiment is affected by both steps. To make the details that we have revealed in this work for both steps clearer, we presented the two models separated by scale - organismal vs. intracellular. We agree that this integrative manuscript appears very broad when the many different findings are each considered separately. The overall model revealed here forms the necessary foundation for the deep analysis of individual aspects in the future.

(2) The large scope of the manuscript denies space to further probe some of the ideas proposed. The first part of the manuscript, particularly Figures 1 and 2, presents data that can be caused by multiple mechanisms, some of which the authors describe in the results but do not test further. Thus, portions of the results text come across as claims that are not supported by the data presented.

We agree that one of the consequences of addressing the joint roles of transport and subsequent silencing during RNAi is that the scope of the manuscript appears large. We had suggested multiple interpretations for specific observations in keeping with the need for further work. To avoid any misunderstandings that our listing of possible interpretations be taken as claims by the reader, we have followed the instructions of the reviewer (see below) and moved some of the potential explanations we raised to the discussion section.

(3) The manuscript focuses on the genetics of SDGs but not the proteins themselves. Few descriptions of the SDGs functions are provided nor is it clarified why only SDG-1 was pursued in imaging and genetic experiments. Additionally, the SDG-1 imaging experiments could use additional localization controls.

We agree that more work on the SDG proteins will likely be informative, but are beyond the scope of this already expansive paper.  We began with the analysis of SDG-1 because it had the most support as a regulator of RNA silencing (Fig. 5f). Indeed, in other work (Lalit and Jose, bioRxiv, 2024), we find that AlphaFold 2 predicts the SDG-1 protein to be a regulator of RNA silencing that directly interacts with the dsRNA-editing enzyme ADR-2 and the endonuclease RDE-8. Furthermore, we expect that more sensitive analyses of the RNA-seq data are likely to reveal additional genes regulated by SID-1. Using multiplexed genome editing, we hope to generate mutant combinations lacking multiple sdg genes to reveal their function(s).

We agree that given the recent discovery of many components of germ granules, our imaging data does not have sufficient resolution to discriminate between them. We have modified our statements and our model regarding the colocalization of SDG-1 with Z-granules to indicate that the overlapping enrichment of SDG-1 and ZNFX-1 in the perinuclear region is consistent with interactions with other nearby granule components.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

Major

(1) As presented, the manuscript is almost two manuscripts combined into one. This point is highlighted in Figure 7h, which basically presents two separate models. The key questions addressed in the manuscript starts at Figure 3. Figures 1 and 2 are interesting observations but require more experiments to define further. For example, as the Results text describes for Figure 1, "These differences in the entry of ingested dsRNA into cells and/or subsequent silencing could be driven by a variety of changes during development. These include changes in the uptake of dsRNA into the intestine, distribution of dsRNA to other tissues from the intestine, import of dsRNA into the germline, and availability of RNA silencing factors within the germline." Presenting these (reasonable) mechanistic ideas detracted from the heritable RNA epigenetic mechanism explored in the later portion of the manuscript. There are many ways to address this issue, one being moving Figures 1 and 2 to the Supplement to focus on SID-1 related pathways.

Since this manuscript addresses the interaction between intercellular transport of dsRNA and heritable epigenetic changes, it was necessary to establish the possible route(s) that dsRNA could take to the germline before any inference could be made regarding heritable epigenetic changes. As suggested below (pt. 2), we have now moved the alternatives we enumerated as possible explanations for some experimental results (e.g., for the differences quoted here) to the discussion section.

(2) The manuscript includes detailed potential interpretations in the Results, making them seem like claims. Here is an example:

"Thus, one possibility suggested by these observations is that reduction of sdg-1 RNA via SID-1 alters the amount of SDG-1 protein, which could interact with components of germ granules to mediate RNA regulation within the germline of wild-type animals."

This mechanism is a possibility, but placing these ideas in the citable results makes it seem like an overinterpretation of imaging data. This text and others should be in the Discussion, where speculation is encouraged. Results sections like this example and others should be moved to the discussion.

We have rephrased motivating connections between experiments like the one quoted above and also moved such text to the discussion section wherever possible.

(3) A paragraph describing the SDG proteins will be helpful. Homologs? Conserved protein domains? mRNA and/or protein expression pattern across worm, not just the germline? Conservation across Caenorhabditis sp? These descriptions may help establish context why SDG-1 localizes to Z-granules.

We have now added information about the conservation of the sdg-1 gene in the manuscript. AlphaFold predicts domains with low confidence for the SDG-1 protein, consistent with the lack of conservation of this protein (AlphaFold requires multiple sequence alignments to predict confidently). In the adult animal, the SDG-1 protein was only detectable in the germline. Future work focused on SDG-1, SDG-2 and other SDG proteins will further examine possible expression in other tissues and functional domains if any. Unfortunately, in multiple attempts of single-molecule FISH experiments using probes against the sdg-1 open reading frame, we were unable to detect a specific signal above background (data not shown). Additional experiments are needed for the sensitive detection of sdg-1 expression outside the germline, if any.  

(4) Based on the images shown, SDG-1 could be in other nearby granules, such as P granules or mutator foci. Additional imaging controls to rule out these granules/condensates will greatly strengthen the argument that SDG-1 protein localizes to Z-granules specifically.

We have modified the final model to indicate that the perinuclear colocalization is with germ granules broadly and we agree that we do not have the resolution to claim that the observed overlap of SDG-1::mCherry with GFP::ZNFX-1 that we detect using Airyscan microscopy is specifically with Z granules. Our initial emphasis of Z-granule was based on the prior report of SDG-1 being co-immunoprecipitated with the Z-granule surface protein PID-2/ZSP-1. However, through other work predicting possible direct interactions using AlphaFold (Lalit and Jose, bioRxiv, 2024), we were unable to detect any direct interactions between PID-2 and SDG-1. Indeed, many additional granules have been recently reported (Chen et al., Nat. Commun., 2024; Huang et al., bioRxiv 2024), making it possible that SDG-1 has specific interactions with a component of one of the other granules (P, Z, M, S, E, or D) or adjacent P bodies.

Minor

(1) "This entry into the cytosol is distinct from and can follow the uptake of dsRNA into cells, which can rely on other receptors." Awkard sentence. Please revise.

We have now revised this sentence to read “This entry into the cytosol is distinct from the uptake of dsRNA into cells, which can rely on other receptors”

(2) Presumably, the dsRNA percent of the in vitro transcribed RNA is different than the 50 bp oligos that can be reliably annealed by heating and cooling. Other RNA secondary structure possibilities warrant further discussion.

We agree that in vitro transcribed RNA could include a variety of undefined secondary structures in addition to dsRNAs of mixed length. Such structures could recruit or titrate away RNA-binding proteins in addition to the dsRNA structures engaging the canonical RNAi pathway, resulting in mixed mechanisms of silencing. Future work identifying such structures and exploring their impact on the efficacy of RNAi could be informative. We have now added these considerations to the discussion and thank the reviewer for highlighting these possibilities.

eLife Assessment

In this report, the authors present valuable findings identifying a novel worm-specific protein (sdg-1) that is induced upon loss of dsRNA import via SID-1, but is not required to mediate SID-1 RNA regulatory effects. The genetic and genomic approaches are well-executed and the revision contain generally solid support for the central findings of the work. These findings will be of interest to those working in the germline epigenetic inheritance field.

Reviewer #1 (Public review):

Summary:<br /> In the manuscript "Intergenerational transport of double-stranded RNA limits heritable epigenetic changes," Shugarts and colleagues investigate intergenerational dsRNA transport in the nematode C. elegans. By inducing oxidative damage, they block dsRNA import into cells, which affects heritable gene regulation in the adult germline (Fig. 2). They identify a novel gene, sid-1-dependent gene-1 (sdg-1), upregulated upon SID-1 inhibition (Fig. 3). Both transient and genetic depletion of SID-1 lead to the upregulation of sdg-1 and a second gene, sdg-2 (Fig. 5). Interestingly, while sdg-1 expression suggests a potential role in dsRNA transport, neither its overexpression nor loss-of-function impacts dsRNA-mediated silencing in the germline (Fig. 7).

Strengths:<br /> • The authors employ a robust neuronal stress model to systematically explore SID-1 dependent intergenerational dsRNA transport in C. elegans.<br /> • They discover two novel SID-1-dependent genes, sdg-1 and sdg-2.<br /> • The manuscript is well-written and addresses the compelling topic of dsRNA signaling in C. elegans.

Weaknesses:<br /> • The molecular mechanism downstream of SDG-1 remains unclear. Testing whether sdg-2 functions redundantly with sdg-1could provide further insights.<br /> • SDG-1 dependent genes in other nematodes remain unknown.

Reviewer #2 (Public review):

Summary:

RNAs can function across cell borders and animal generations as sources of epigenetic information for development and immunity. The specific mechanistic pathways how RNA travels between cells and progeny remains an open question. Here, Shugarts, et al. use molecular genetics, imaging, and genomics methods to dissect specific RNA transport and regulatory pathways in the C. elegans model system. Larvae ingesting double-stranded RNA is noted to not cause continuous gene silencing throughout adulthood. Damage of neuronal cells expressing double-stranded target RNA is observed to repress target gene expression in the germline. Exogenous short or long double-stranded RNA required different genes for entry into progeny. It was observed that the SID-1 double-stranded RNA transporter showed different expression over animal development. Removal of the sid-1 gene caused upregulation of two genes, the newly described sid-1-dependent gene sdg-1 and sdg-2. Both genes were observed to be negatively regulated by other small RNA regulatory pathways. Strikingly, loss then gain of sid-1 through breeding still caused variability of sdg-1 expression for many, many generations. SDG-2 protein co-localizes with germ granules, intracellular sites for heritable RNA silencing machinery. Collectively, sdg-1 presents a model to study how extracellular RNAs can buffer gene expression in germ cells and other tissues.

Strengths:

(1) Very cleaver molecular genetic methods and genomic analyses, paired with thorough genetics, were employed to discover insights into RNA transport, sdg-1 and sdg-2 as sid-1-dependent genes, and sdg-1's molecular phenotype.

(2) The manuscript is well cited, and figures reasonably designed.

(3) The discovery of the sdg genes being responsive to the extracellular RNA cell import machinery provides a model to study how exogenous somatic RNA is used to regulate gene expression in progeny. The discovery of genes within retrotransposons stimulates tantalizing models how regulatory loops may actually permit the genetic survival of harmful elements.

Weaknesses:

(1) The manuscript is broad, making it challenging to read and consider the data presented. Of note, since the original submission, the authors have improved the clarity of the writing and presentation.

Comments on revised version:

This reviewer thanks the authors for their efforts in revising the manuscript. In their rebuttal, the authors acknowledged the broad scope of their manuscript. I concur. While I still think the manuscript is a challenge to read due to its expansive nature, the current draft is substantially improved when compared to the previous one. This work will contribute to our general knowledge of RNA biology, small RNA regulatory pathways, and RNA inheritance.

eLife Assessment

This is a valuable study regarding the role of gasdesmin D in experimental psoriasis. The study contains solid evidence for such a role, involving neutrophils, from murine models of skin inflammation, as well as correlative data of elevated gasdermin D expression in human psoriatic skin. The findings will be of interest to researchers trying to unravel pathways of skin inflammation.

Reviewer #1 (Public review):

Summary:

Recommendations for the authors In this study, Liu, Jiang, Diao et.al. investigated the role of GSDMD in psoriasis-like skin inflammation in mice. The authors have used full-body GSDMD knock-out mice and Gsdm floxed mice crossed with the S100A8- Cre. In both mice, the deficiency of GSDMD ameliorated the skin phenotype induced by the imiquimod. The authors also analyzed RNA sequencing data from the psoriatic patients to show an elevated expression of GSDMD in the psoriatic skin.

Strengths:

It has the potential to unravel the new role of neutrophils.

Comments on revisions:

The authors have addressed the majority of comments and concerns and highlighted the potential limitations wherever not possible.

Reviewer #2 (Public review):

Summary:

The authors describe elevated GSDMD expression in psoriatic skin, and knock-out of GSDMD abrogates psoriasis-like inflammation.

Strengths:

The study is well conducted with transgenic mouse models. Using mouse-models with GSDMD knock-out showing abrogating inflammation, as well as GSDMD fl/fl mice without neutrophils having a reduced phenotype.

My major concern would be the involvement of other inflammasome and GSDMD bearing cell types, esp. Keratinocytes (KC), which could be an explanation why the experiments in Fig 4 still show inflammation.

Comments on revisions:

The authors have sufficiently addressed my questions.

Author response:

The following is the authors’ response to the original reviews.

eLife Assessment

This is a potentially interesting study regarding the role of gasdesmin D in experimental psoriasis. The study contains useful data from murine models of skin inflammation, however the main claims (on neutrophil pyroptosis) are incompletely supported in its current form and require additional experimental support to justify the conclusions made.

We sincerely appreciate the positive assessment regarding the significance of our study, as well as the valuable suggestions provided by the reviewers. We have included new data, further discussions and clarifications in the revised manuscript to adequately address all the concerns raised by the reviewers and better support our conclusions.

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this study, Liu, Jiang, Diao et.al. investigated the role of GSDMD in psoriasis-like skin inflammation in mice. The authors have used full-body GSDMD knock-out mice and Gsdm floxed mice crossed with the S100A8- Cre. In both mice, the deficiency of GSDMD ameliorated the skin phenotype induced by the imiquimod. The authors also analyzed RNA sequencing data from the psoriatic patients to show an elevated expression of GSDMD in the psoriatic skin.

Overall, this is a potentially interesting study, however, the manuscript in its current format is not completely a novel study.

Strengths:

It has the potential to unravel the new role of neutrophils.

Weaknesses:

The main claims are only partially supported and have scope to improve

We thank the reviewer for the positive evaluation of the interest and potential of our work. In response to reviewers’ suggestions, we have added new content, including additional data and discussions, to further demonstrate the important role of GSDMD-mediated neutrophil pyroptosis in the pathogenesis of psoriasis, thereby enhancing the completeness of our research.

Reviewer #2 (Public review):

Summary:

The authors describe elevated GSDMD expression in psoriatic skin, and knock-out of GSDMD abrogates psoriasis-like inflammation.

Strengths:

The study is well conducted with transgenic mouse models. Using mouse-models with GSDMD knock-out showing abrogating inflammation, as well as GSDMD fl/fl mice without neutrophils having a reduced phenotype.

I fear that some of the conclusions cannot be drawn by the suggested experiments. My major concern would be the involvement of other inflammasome and GSDMD bearing cell types, esp. Keratinocytes (KC), which could be an explanation why the experiments in Fig 4 still show inflammation.

Weaknesses:

The experiments do not entirely support the conclusions towards neutrophils.

We appreciate the reviewers’ positive evaluation regarding the application of our mouse models. We also thank the reviewers for insightful comments and suggestions that can improve the quality of our work. Addressing these issues has significantly strengthened our conclusions. Our responses to the above questions are as follows.

Specific questions/comments:

Fig 1b: mainly in KC and Neutrophils?

In Figure 1b, we observed that GSDMD expression is higher in the psoriasis patient tissues compared to control samples. As the role of GSDMD in keratinocytes during the pathogenesis of psoriasis has already been explored[1], we focused our study on GSDMD in neutrophils. In response to the comments, we have added co-staining results of the neutrophil marker CD66b and GSDMD in the revised manuscript (see new Figure 3b in the revised manuscript). This addition further substantiates the expression of GSDMD in neutrophils within psoriasis tissue.

Fig 2a: PASI includes erythema, scaling, thickness and area. Guess area could be trick, esp. in an artificial induced IMQ model (WT) vs. the knock-out mice.

In our model, to accurately assess the disease condition in mice, we standardized the drug treatment area on the dorsal side (2*3 cm). Therefore, the area was not factored into the scoring process, and we have included a detailed description of this in the revised manuscript.

Fig 2d: interesting finding. I thought that CASP-1 is cleaving GSDMD. Why would it be downregulated?

Regarding the downregulation of CASP in GSDMD KO mouse skin tissue, existing studies indicate that GSDMD generates a feed-forward amplification cascade via the mitochondria-STING-Caspase axis [2]. We hypothesize that the absence of GSDMD attenuates STING signaling’s activation of Caspase.

Line 313: as mentioned before (see Fig 1b). KC also show a stron GSDMD staining positivity and are known producers of IL-1b and inflammasome activation. Guess here the relevance of KC in the whole model needs to be evaluated.

Our research primarily focuses on the role of neutrophil pyroptosis in psoriasis, this does not conflict with existing reports indicating that KC cell pyroptosis also contributes to disease progression[1]. Both studies underscore the significant role of GSDMD-mediated pyroptotic signaling in psoriasis, and the consistent involvement of KC cells and neutrophils further emphasizes the potential therapeutic value of targeting GSDMD signaling in psoriasis treatment. We have expanded upon this discussion in the revised manuscript.

Fig 4i - guess here the conclusion would be that neutrophils are important for the pathogenesis in the IMQ model, which is true. This experiment does not support that this is done by pyroptosis.

To address the question, we analyzed the publicly available single-cell transcriptomic data (GSE165021) and found that, compared to the control group, neutrophils infiltrating in IMQ-induced psoriasis-like tissue display a higher expression of pyroptosis-related genes (see new Figure 3e in the revised manuscript). These results strengthen our conclusions about the role of neutrophil pyroptosis in the progression of psoriasis.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Specific Comments:

• Figure 1: Micro abscesses would already be dead, which would likely reflect as non-specific staining. Authors should consider double staining (e.g., GSDMD+Ly6G).

We thank the reviewer for the useful suggestion. We have added co-staining results of the neutrophil marker CD66b and GSDMD in the revised manuscript (see new Figure 3b in the revised manuscript). This addition further substantiates the expression of GSDMD in neutrophils within psoriasis tissue.

• Figures 1 b, c, and d do not have the n number for representative experiments and images.

We apologize for our oversight. We have added the relevant information in the revised manuscript and have reviewed and corrected the entire text.

• What is the difference between psoriasis patients in Figure 1 versus Figure 3 as the staining patterns are different? It is difficult to interpret from Figure 1 that expression is limited to neutrophils. Authors should consider double staining (e.g., GSDMD+Ly6G). How many samples were stained to draw this conclusion?

We thank the reviewer for the suggestion. In Figure 1b, we observed that GSDMD expression is higher in the psoriasis patient tissues compared to control samples. We have added co-staining results of the neutrophil marker CD66b and GSDMD in the revised manuscript (see new Figure 3b in the revised manuscript). For each staining group, we examined samples from 3-5 patients to draw the conclusion.

• Figure 2: GSDMD deficiency mitigates psoriasis-like inflammation in mice has been shown before (PMID#37673869). The paper showed that the GSDMD was mainly expressed in keratinocytes. What is the view of the authors on it and how does this data correlate with the data presented in this manuscript by the authors?

Consistent with previous studies[1], we observed increased expression of pyroptosis-related proteins in psoriatic lesions. However, our research focused specifically on the role of neutrophil pyroptosis in psoriasis, this does not conflict with existing reports indicating that KC cell pyroptosis also contributes to disease progression. Both studies underscore the significant role of GSDMD-mediated pyroptotic signaling in psoriasis, and the consistent involvement of KC cells and neutrophils further emphasizes the potential therapeutic value of targeting GSDMD signaling in psoriasis treatment. We have expanded upon this discussion in the revised manuscript.

• Figure 3d: It is unclear if the IF shows an epidermal or dermal area. As shown by authors in other figures (human psoriatic skin), do authors observe more GSDMD in the micro abscess, which is localized in the epidermis? The authors should also show the staining of GSDM/Ly6G in the whole skin sample.

The region we presented for immunofluorescence staining corresponds to the dermis of the mice, as we did not observe typical neutrophil micro abscesses similar to those in human psoriasis in the epidermis of IMQ-induced classical psoriasis vulgaris (PV) model. Therefore, we have only shown the staining in the dermal area.

• Figure 3e: PI staining also represents necrotic cells and TUNEL staining would not represent just apoptotic cells. It is unclear how the authors conclude an ongoing pyroptosis in neutrophils. A robust dataset is needed to provide evidence supporting neutrophil pyroptosis in the IMQ-challenged mice.

We thank the reviewer for the valuable suggestion. GSDMD is the effector protein of pyroptosis. To further confirm that cells are undergoing pyroptosis, it is necessary to morphologically stain the GSDMD N-terminal protein. Although there is currently no GSDMD N-terminal fluorescent antibody available, we detected the cleaved N-terminus of GSDMD by WB in mouse psoriasis-like skin tissue, and its increased expression suggested increased cell pyroptosis (see new Figure 1d in the revised manuscript). Moreover, we analyzed the publicly available single-cell transcriptomic data (GSE165021) and found that, compared to the control group, neutrophils infiltrating in IMQ-induced psoriasis-like tissue display a higher expression of pyroptosis-related genes (see new Figure 3e in the revised manuscript). These results strengthen our conclusions about the role of neutrophil pyroptosis in the progression of psoriasis.

• Figure 4: The authors did not clarify the reason for choosing D4 over the usual D7 for the imiquimod experiment. S100A8-Cre is also reported in monocytes and granulocytes/monocyte progenitors. And, the authors also show the expression in macrophages and neutrophils, but in the text, only neutrophils are mentioned. The authors should state the results in the text as well to avoid misrepresentation of the data.

We thank the reviewer for the useful suggestion. We have repeated many times of experiments in our previous studies and observed that the IMQ-induced mouse psoriasis model showed the obvious signs of self-resolution after Day 4 even with continuing topical IMQ application, thus we chose 4 days over 7 days for the imiquimod experiment, which are consistent with many other studies[3, 4].

Many studies use S100A8-Cre mice for neutrophil-specific gene knockout[5, 6]. Moreover, we used Ly6G antibody to eliminate neutrophils in GSDMD-cKO mice and control mice. It was found that the difference in lesions between the two groups was abolished after neutrophil depletion, indicating that neutrophil pyroptosis plays an important role in the pathogenesis of imiquimod-induced psoriasis-like lesions in mice. As the database analysis results showed that macrophages have slight expression of S100a8, according to the suggestion of the reviewer, we have added a more precise description in the revised manuscript.

• Figure S2a: Ly6G antibody reduced the ly6G positive, but also negative cells compared to PBS. If this is correct, what is the explanation, and how this observation has been considered for concluding results?

Neutrophils play an important role in regulating inflammatory responses, and their deletion can reduce the overall inflammatory level in the body, which also results in a decrease in other non-neutrophil cells. However, this change does not affect our conclusions. Our results show that after the deletion of neutrophils, there is no difference in the pathological manifestations between the cKO group and the control group. This further that GSDMD in neutrophil plays an important role in the pathogenesis of miquimod-induced psoriasis-like lesions in mice.

• The conclusion in Figure 4i is incorrect as Ly6G administration had an effect on the wt, so it shows neutrophils play a role, but not neutrophil pyroptosis.

- 321 "It was found that the difference in lesions between the

- 321 two groups was abolished after neutrophil depletion (Fig4i, S2a), indicating that

- 322 neutrophil pyroptosis plays an important role in the pathogenesis of

- 323 imiquimod-induced psoriasis-like lesions in mice"

Our results show that after the deletion of neutrophils, there is no difference in the pathological manifestations between the cKO group and the control group. This further indicates that the lower disease scores observed in cKO mice, in the absence of neutrophil deletion, depend on the presence of neutrophils. In the revised manuscript, we have changed the statement to “It was found that the difference in lesions between the two groups was abolished after neutrophil depletion (Fig4i, S2a), indicating that GSDMD in neutrophil plays an important role in the pathogenesis of miquimod-induced psoriasis-like lesions in mice”

• The effect of LyG Ab: reduced PASI in the wt, but the effect on the ko remains the same. What are the other molecular changes observed? What was the level of neutrophils in the wt and the S1A008Cre GsdmDfl/fl mice under steady state and how are they change upon imiquimod challenge? A complete profiling of the immune cells is needed for all the experiments.

As demonstrated by the results, the deletion of neutrophils did not significantly alter the pathological phenotype of cKO mice. We believe that this outcome precisely highlights the crucial role of GSDMD in regulating neutrophil inflammatory responses.

• Figure S2b: The authors conclude that Il-1b in the imiquimod skin is mainly expressed by neutrophils, but the analysis presented in the figure does not support this conclusion. Both neutrophils and macrophages are majorly positive for I1-b, with some expression on Langerhans and fibroblasts. No n numbers are provided for the experiment

As we discussed in the manuscript, we speculate that neutrophil pyroptosis may release cytokines, which in turn activate other cells to secrete cytokines, forming a complex inflammatory network in psoriasis. This may suggest that neutrophil pyroptosis may be involved in the pathogenesis of psoriasis by affecting the secretion of cytokines such as IL-1B and IL-6 by neutrophils, thereby affecting the function of other immune cells such as T cells and macrophages.

We have added the n number in the revised manuscript.

• For clarity and transparency, a list of antibodies with the associate clone and catalogue number should be provided or integrated into the method text.

We thank the reviewer for the useful suggestion. We have added the associate clone and catalogue number of antibodies used in the method text of revised manuscript.

Reviewer #2 (Recommendations for the authors):

Fig 3b: psoriasis and pustular psoriasis have a different pathophysiology (autoimmune vs. autoinflammatory). Neutrophils are centrally important for GPP for the cleavage of IL-36. Guess as not further referred to pustular psoriasis in the paper, that comparison is rather deviating from the story.

In Figure 3b, we stained for GSDMD and CD66b in both plaque psoriasis (PV) and generalized pustular psoriasis (GPP), not to compare the expression differences between the two types of psoriasis, but rather to demonstrate that significant GSDMD expression is present in neutrophils in different types of psoriasis. Unfortunately, due to the lack of a well-established animal model for GPP, we were only able to conduct studies using the established PV animal model. We acknowledge this limitation in our research. In our revised manuscript, we have added the following explanation in the discussion section: “Although we observed significantly increased GSDMD in neutrophils in pustular psoriasis, we were constrained to studying the established PV animal model due to the current absence of a mature GPP animal model. This represents a limitation of our study.”

In summary, we appreciate the Reviewer’s comments and suggestions. We feel that the inclusion of new data addresses the concerns in a comprehensive manner and adds further support to our original conclusions. We hope you will now consider the revised manuscript worthy of publication in eLife.

References:

(1) Lian, N., et al., Gasdermin D-mediated keratinocyte pyroptosis as a key step in psoriasis pathogenesis. Cell Death & Disease, 2023. 14(9): p. 595.

(2) Han, J., et al., GSDMD (gasdermin D) mediates pathological cardiac hypertrophy and generates a feed-forward amplification cascade via mitochondria-STING (stimulator of interferon genes) axis. Hypertension, 2022. 79(11): p. 2505-2518.

(3) Lin, H., et al., Forsythoside A alleviates imiquimod-induced psoriasis-like dermatitis in mice by regulating Th17 cells and IL-17a expression. Journal of Personalized Medicine, 2022. 12(1): p. 62.

(4) Emami, Z., et al., Evaluation of Kynu, Defb2, Camp, and Penk Expression Levels as Psoriasis Marker in the Imiquimod‐Induced Psoriasis Model. Mediators of Inflammation, 2024. 2024(1): p. 5821996.

(5) Stackowicz, J., et al., Neutrophil-specific gain-of-function mutations in Nlrp3 promote development of cryopyrin-associated periodic syndrome. Journal of Experimental Medicine, 2021. 218(10): p. e20201466.

(6) Abram, C.L., et al., Distinct roles for neutrophils and dendritic cells in inflammation and autoimmunity in motheaten mice. Immunity, 2013. 38(3): p. 489-501.

eLife Assessment

This important work is a versatile new addition to the chemical protein modifications and bioconjugation toolbox in synthetic biology. The technology developed cleverly uses Connectase to irreversibly fuse proteins of interest together so they can be studied in their native context, with convincing data showing the technique works for various protein partners. This work will help multiple fields to explore multi-function constructs in basic synthetic biology. This work will also be of interest to those studying fusion oncoproteins commonly expressed in various human pathologies.

Reviewer #1 (Public review):

Fuchs describes a novel method of enzymatic protein-protein conjugation using the enzyme Connectase. The author is able to make this process irreversible by screening different Connectase recognition sites to find an alternative sequence that is also accepted by the enzyme. They are then able to selectively render the byproduct of the reaction inactive, preventing the reverse reaction, and add the desired conjugate with the alternative recognition sequence to achieve near-complete conversion. I agree with the authors that this novel enzymatic protein fusion method has several applications in the field of bioconjugation, ranging from biophysical assay conduction to therapeutic development. Previously the author has published on the discovery of the Connectase enzymes and has shown its utility in tagging proteins and detecting them by in-gel fluorescence. They now extend their work to include the application of Connectase in creating protein-protein fusions, antibody-protein conjugates, and cyclic/polymerized proteins. As mentioned by the author, enzymatic protein conjugation methods can provide several benefits over other non-specific and click chemistry labeling methods. Connectase specifically can provide some benefits over the more widely used Sortase, depending on the nature of the species that is desired to be conjugated. However, due to a similar lengthy sequence between conjugation partners, the method described in this paper does not provide clear benefits over the existing SpyTag-SpyCatcher conjugation system. Additionally, specific disadvantages of the method described are not thoroughly investigated, such as difficulty in purifying and separating the desired product from the multiple proteins used. Overall, this method provides a novel, reproducible way to enzymatically create protein-protein conjugates.

The manuscript is well-written and will be of interest to those who are specifically working on chemical protein modifications and bioconjugation.

Reviewer #2 (Public review):

Summary:

Unlike previous traditional protein fusion protocols, the author claims their proposed new method is fast, simple, specific, reversible, and results in a complete 1:1 fusion. A multi-disciplinary approach from cloning and purification, biochemical analyses, and proteomic mass spec confirmation revealed fusion products were achieved.

Strengths:

The author provides convincing evidence that an alternative to traditional protein fusion synthesis is more efficient with 100% yields using connectase. The author optimized the protocol's efficiency with assays replacing a single amino acid and identification of a proline aminopeptidase, Bacilius coagulans (BcPAP), as a usable enzyme to use in the fusion reaction. Multiple examples including Ubiquitin, GST, and antibody fusion/conjugations reveal how this method can be applied to a diverse range of biological processes.

Weaknesses:

Though the ~100% ligation efficiency is an advancement, the long recognition linker may be the biggest drawback. For large native proteins that are challenging/cannot be synthesized and require multiple connectase ligation reactions to yield a complete continuous product, the multiple interruptions with long linkers will likely interfere with protein folding, resulting in non-native protein structures. This method will be a good alternative to traditional approaches as the author mentioned but limited to generating epitope/peptide/protein tagged proteins, and not for synthetic protein biology aimed at examining native/endogenous protein function in vitro.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Fuchs describes a novel method of enzymatic protein-protein conjugation using the enzyme Connectase. The author is able to make this process irreversible by screening different Connectase recognition sites to find an alternative sequence that is also accepted by the enzyme. They are then able to selectively render the byproduct of the reaction inactive, preventing the reverse reaction, and add the desired conjugate with the alternative recognition sequence to achieve near-complete conversion. I agree with the authors that this novel enzymatic protein fusion method has several applications in the field of bioconjugation, ranging from biophysical assay conduction to therapeutic development. Previously the author has published on the discovery of the Connectase enzymes and has shown its utility in tagging proteins and detecting them by in-gel fluorescence. They now extend their work to include the application of Connectase in creating protein-protein fusions, antibody-protein conjugates, and cyclic/polymerized proteins. As mentioned by the author, enzymatic protein conjugation methods can provide several benefits over other non-specific and click chemistry labeling methods. Connectase specifically can provide some benefits over the more widely used Sortase, depending on the nature of the species that is desired to be conjugated. However, due to a similar lengthy sequence between conjugation partners, the method described in this paper does not provide clear benefits over the existing SpyTag-SpyCatcher conjugation system.  Additionally, specific disadvantages of the method described are not thoroughly investigated, such as difficulty in purifying and separating the desired product from the multiple proteins used. Overall, this method provides a novel, reproducible way to enzymatically create protein-protein conjugates.

The manuscript is well-written and will be of interest to those who are specifically working on chemical protein modifications and bioconjugation.

Reviewer #2 (Public review):

Summary:

Unlike previous traditional protein fusion protocols, the author claims their proposed new method is fast, simple, specific, reversible, and results in a complete 1:1 fusion. A multi-disciplinary approach from cloning and purification, biochemical analyses, and proteomic mass spec confirmation revealed fusion products were achieved.

Strengths:

The author provides convincing evidence that an alternative to traditional protein fusion synthesis is more efficient with 100% yields using connectase. The author optimized the protocol's efficiency with assays replacing a single amino acid and identification of a proline aminopeptidase, Bacilius coagulans (BcPAP), as a usable enzyme to use in the fusion reaction. Multiple examples including Ubiquitin, GST, and antibody fusion/conjugations reveal how this method can be applied to a diverse range of biological processes.

Weaknesses:

Though the ~100% ligation efficiency is an advancement, the long recognition linker may be the biggest drawback. For large native proteins that are challenging/cannot be synthesized and require multiple connectase ligation reactions to yield a complete continuous product, the multiple interruptions with long linkers will likely interfere with protein folding, resulting in non-native protein structures. This method will be a good alternative to traditional approaches as the author mentioned but limited to generating epitope/peptide/protein tagged proteins, and not for synthetic protein biology aimed at examining native/endogenous protein function in vitro.

I would like to sincerely thank both reviewers for their insightful and constructive feedback on the manuscript. I have addressed reviewer #1’s comments below:

(1) The benefits over the SpyTag-SpyCatcher system. Here, the conjugation partners are fused via the 12.3 kDa SpyCatcher protein, which is considerably larger than the Connectase fusion sequence (20 aa). This is briefly mentioned in the introduction (p. 1 ln 24-25). In a related technology, the SpyTag-SpyCatcher system was split into three components, SpyLigase, SpyTag and KTag  (Fierer et al., PNAS 2014). The resulting method introduces a sequence between the fusion partners (SpyTag (13aa) + KTag (10aa)), which is similar in length to the Connectase fusion sequence. I mention this method in the discussion (p. 8, ln 296 - 297), but preferred not to comment on its efficiency. It appears to require more enzyme and longer incubation times, while yielding less fusion product (Fierer et al., Figure 2).

(2) Purification of the fusion product. The method is actually advantageous in this respect, as described in the discussion (p. 8, ln 257-263). I plan to add a figure showing an example in the revised article.

eLife Assessment

This study investigated mitochondrial dysfunction and the impairment of the ciliary Sonic Hedgehog signaling in Lowe syndrome (LS), a timely topic given the limited research in this area. The data from patient iPSC-derived neurons and a mouse model were collected using solid methods, but the evidence supporting key claims is incomplete, and some technical aspects fall short of expectations. Despite these limitations, the study provides a useful foundation for exploring the relationship between mitochondrial defects and primary cilia in neural development.

Reviewer #1 (Public review):

Summary:

This study by Lo et al. seeks to explain the cellular defects underlying the brain phenotypes of Lowe syndrome (LS). There have been limited studies on this topic and hence this is a timely study.

Strengths:

Studies such as these can contribute to an understanding of the cellular and developmental mechanisms of brain disorders.

Weaknesses:

This study by Lo et al. seeks to explain the cellular defects underlying the brain phenotypes of Lowe syndrome (LS). There have been limited studies on this topic and hence this is a timely study.

The study uses two models: (1) an LS IOB knockout mouse and (2) neurons derived from iPSC lines from LS patients. These two models are used to present three separate findings: (1) altered mitochondria function, (2) altered numbers of neurons and glia in both models, and (3) some evidence of altered Sonic Hedgehog signaling projected as a defect in cilia.

Conceptually, there are some problems of serious concern which must be carefully considered:<br /> (1) The IOB mouse was very extensively phenotyped when it was generated by Festa et.al HMM, 2019. It does not have any obvious phenotypes of brain deficits although the studies in this paper were very detailed indeed.<br /> (2) Reduced brain size is reported as a phenotype of the IOB mouse in this study. Yet over the many clinical studies of LS published over the years, altered brain size has not been noted, either in clinical examination or in the many MRI reports of LS patients.

While reading through these results it is striking that the link between the three reported phenotypes is at least tenuous, and in fact may not exist at all. The link between mitochondria and neurogenesis is based on a single paper that has been cited incorrectly and out of context. There is no evidence presented for a link between the Shh signaling defect reported and the mitochondrial phenotype.

General comments

(1) The preparation of the manuscript requires improvement. There are many errors in the presentation of data.<br /> (2) The use of references needs to be re-considered. Sometimes a reference is used when in fact the results included in that paper are the opposite of what the authors intend.<br /> (3) The authors conclude the paper by claiming that mitochondrial dysfunction and impairments of the ciliary SHH contribute to abnormal neuronal differentiation in LS, but the mechanism by which this sequence of events might happen hasn't been shown.

Final comments:

(1) Phenotype of increased astrocytes:<br /> The phenotype of increased astrocytes in both the IOB mouse brain or iPSC-derived cultures iN cells requires clarification as one of the markers used as an astrocyte marker, BRN2, is commonly used as a neuronal marker. As LS is a neurodevelopmental disorder, and the phenotype in question is related to differentiation, it is crucial to shed light on the developmental timeline in which this phenotype is seen in the mouse brain.

(2) Ciliary homeostasis:<br /> Mitochondrial dysfunction in astrocytes has been shown to induce a ciliogenic program. However, almost the opposite is shown in this paper, with regards to ciliation. Morphology of the cilia was not assessed either, which is an important feature of ciliary homeostasis. The improper ciliary homeostasis here appears to be the improper Shh signalling, which has not been shown to be related to mitochondrial dysfunction. This leaves one wondering how exactly the different phenotypes shown in this paper are connected.

(3) This paper lacks a clear mechanistic approach. While the data validates the 3 broad phenotypes mentioned, there is a lack of connection between these phenotypes or an answer to why these phenotypes appear. While the discussion attempts to shed light on this by referencing previous studies, some of the referenced studies show contradicting results. Hence, it would be beneficial to clarify these gaps with further experiments and address the larger question of the connection between the mitochondria, Shh signalling, and astrocyte formation.

(4) Most importantly, there is no mention of how the loss of OCRL, a 5-phosphatase enzyme, results in the appearance of the mentioned phenotypes. Since there are multiple studies in the field of Lowe Syndrome that shed light on the various functions of OCRL, both catalytic and non-catalytic, it is important to address the role of OCRL in resulting in these phenotypes.

(5) There are numerous errors in the qPCR experiments performed with regard to the genes that were assayed. The genes mentioned in the text section do not match those indicated in the graphs or legends. This takes away the confidence of the reader in this data.

Reviewer #2 (Public review):

Summary:

This manuscript investigates how neural cell development is affected in Lowe syndrome. Using neural cultures differentiated from human iPSCs carrying either an LS mutation or a genetically engineered mutation in OCRL, the authors show a depletion of mitochondrial DNA and a decrease in mitochondrial activities that correlate with an increased formation of astrocytes at the expense of neurons. Similar effects on mitochondria and on astrocyte development were observed in an LS mouse model. Moreover, these mutant brain cells are less likely to be ciliated and show a reduction in Sonic Hedgehog signalling.

Strengths/Weaknesses:

The study derives strength from the analyses of two different models of Lowe syndrome, both reaching similar conclusions. However, the observed changes in mitochondrial defects, neuronal/astrocytic development, and primary cilia are only correlated, with no attempt to investigate a causal relationship. Moreover, the mouse model is only analysed at the adult stage providing no insights into the development of the defects. Different brain regions are analysed with immunostainings and qRT-PCR making it challenging to draw clear correlations between these findings. The quality of the corresponding figures is often poor and the selection of markers is frequently inappropriate. Taken together, these limitations complicate the interpretations of the data and significantly limit the conclusions that can be drawn from the study.

eLife Assessment

This valuable study proposes a theoretical model of clathrin coat formation based on membrane elasticity that seeks to determine whether this process occurs by increasing the area of a protein-coated patch with constant curvature, or by increasing the curvature of a protein-coated patch that forms in an initially flat conformation (so called constant curvature or constant area models). Identifying energetically favorable pathways and comparing the obtained shapes with experiments provides solid support to the constant-area pathway. This work will be of interest for biologists and biophysicists interested in membrane remodelling and endocytosis. It provides an innovative approach to tackle the question of constant curvature vs. constant area coat protein formation, although some of the model's assumption are only partially supported by experimental evidence.

Reviewer #1 (Public review):

Summary:

The authors develop a set of biophysical models to investigate whether a constant area hypothesis or a constant curvature hypothesis explains the mechanics of membrane vesiculation during clathrin-mediated endocytosis.

Strengths:

The models that the authors choose are fairly well-described in the field and the manuscript is well-written.

Weaknesses:

One thing that is unclear is what is new with this work. If the main finding is that the differences are in the early stages of endocytosis, then one wonders if that should be tested experimentally. Also, the role of clathrin assembly and adhesion are treated as mechanical equilibrium but perhaps the process should not be described as equilibria but rather a time-dependent process. Ultimately, there are so many models that address this question that without direct experimental comparison, it's hard to place value on the model prediction.<br /> While an attempt is made to do so with prior published EM images, there is excessive uncertainty in both the data itself as is usually the case but also in the methods that are used to symmetrize the data. This reviewer wonders about any goodness of fit when such uncertainty is taken into account.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors employ theoretical analysis of an elastic membrane model to explore membrane vesiculation pathways in clathrin-mediated endocytosis. A complete understanding of clathrin-mediated endocytosis requires detailed insight into the process of membrane remodeling, as the underlying mechanisms of membrane shape transformation remain controversial, particularly regarding membrane curvature generation. The authors compare constant area and constant membrane curvature as key scenarios by which clathrins induce membrane wrapping around the cargo to accomplish endocytosis. First, they characterize the geometrical aspects of the two scenarios and highlight their differences by imposing coating area and membrane spontaneous curvature. They then examine the energetics of the process to understand the driving mechanisms behind membrane shape transformations in each model. In the latter part, they introduce two energy terms: clathrin assembly or binding energy, and curvature generation energy, with two distinct approaches for the latter. Finally, they identify the energetically favorable pathway in the combined scenario and compare their results with experiments, showing that the constant-area pathway better fits the experimental data.

Strengths:

The manuscript is well-written, well-organized, and presents the details of the theoretical analysis with sufficient clarity.<br /> The calculations are valid, and the elastic membrane model is an appropriate choice for addressing the differences between the constant curvature and constant area models.<br /> The authors' approach of distinguishing two distinct free energy terms-clathrin assembly and curvature generation-and then combining them to identify the favorable pathway is both innovative and effective in addressing the problem.<br /> Notably, their identification of the energetically favorable pathways, and how these pathways either lead to full endocytosis or fail to proceed due to insufficient energetic drives, is particularly insightful.

Weaknesses:

Membrane remodeling in cellular processes is typically studied in either a constant area or constant tension ensemble. While total membrane area is preserved in the constant area ensemble, membrane area varies in the constant tension ensemble. In this manuscript, the authors use the constant tension ensemble with a fixed membrane tension, σe. However, they also use a constant area scenario, where 'area' refers to the surface area of the clathrin-coated membrane segment. This distinction between the constant membrane area ensemble and the constant area of the coated membrane segment may cause confusion.

As mentioned earlier, the theoretical analysis is performed in the constant membrane tension ensemble at a fixed membrane tension. The total free energy E_tot of the system consists of membrane bending energy E_b and tensile energy E_t, which depends on membrane tension, σe. Although the authors mention the importance of both E_b and E_t, they do not present their individual contributions to the total energy changes. Comparing these contributions would enable readers to cross-check the results with existing literature, which primarily focuses on the role of membrane bending rigidity and membrane tension.

The authors introduce two different models, (1,1) and (1,2), for generating membrane curvature. Model 1 assumes a constant curvature growth, corresponding to linear curvature growth, while Model 2 relates curvature growth to its current value, resembling exponential curvature growth. Although both models make physical sense in general, I am concerned that Model 2 may lead to artificial membrane bending at high curvatures. Normally, for intermediate bending, ψ > 90, the bending process is energetically downhill and thus proceeds rapidly. the bending process is energetically downhill and thus proceeds rapidly. However, Model 2's assumption would accelerate curvature growth even further. This is reflected in the endocytic pathways represented by the green curves in the two rightmost panels of Fig. 4a, where the energy steeply increases at large ψ. I believe a more realistic version of Model 2 would require a saturation mechanism to limit curvature growth at high curvatures.

eLife Assessment

This study provides valuable quantitative data and analysis that reveals variations in 'Dorsal' nuclear dynamics along the dorso-ventral axis in the early Drosophila embryo. The evidence that supports that these variations are due to Dorsal/Cactus interactions in dorsal nuclei is convincing, albeit incomplete to understand the biological implications of these findings for developmental patterning.

Reviewer #1 (Public review):

Summary:

Al Asafen and colleagues apply a set of scanning fluorescence correlation spectroscopic approaches (Raster Image Correlation Spectroscopy (RICS), cross-correlation RICS, and pair-correlation function spectroscopy) to address the nuclear-cytoplasmic kinetics of the Dorsal (Dl) transcription factor in early Drosophila embryos. The Toll/Dl system has long been appreciated to establish dorsal-ventral polarity of the embryo through Toll-dependent control of Dl nuclear localization, and provides an example of a morphogen gradient produced with high enough precision to yield robust biophysical measurements of general transcription factor activity and function. By measuring GFP-tagged Dl protein, either in wild-type embryos or in mutant embryos with low/medium/high levels of Toll signaling, the authors report diffusivity of Dl in nuclear and cytoplasmic compartments of the embryo, as well as the fraction of mobile and immobile Dl, which can be correlated with DNA binding through cross-correlation RICS. A model is presented where Cactus/IkB is implicated in preventing Dl from binding to DNA.

Strengths:

The experiments on wild-type GFP-tagged Dorsal are performed well, are mostly reported well, and are interpreted fairly.

Weaknesses:

The discrepancy between experiment and theory as pertains to Michaelis-Menten kinetics is not fully motivated in the text, and could benefit from a more clear presentation. The experiments performed to distinguish between the contribution of Toll-dependent phosphorylation and Cactus interaction models for limiting Dorsal DNA binding are possibly confounded by the presence of wild-type, GFP-tagged Dorsal protein.

Reviewer #2 (Public review):

Summary:

In this manuscript, Al Asafen, Clark et al., use fluorescence correlation spectroscopy (FCS) to quantitatively analyze the mobility of Dl along the DV axis of the early Drosophila embryo. Dl is essential for dorsal-ventral (DV) patterning and its gradient initiates the activation of several genes and thereby orchestrates the formation of the Drosophila body plan. While the mechanisms underlying the formation of the Dl gradient have been extensively studied by this group and others, there are some observations for which there is not yet a mechanistic explanation. For example, the peak of the Dl gradient grows continuously during nuclear cycles 10-14. This is likely due to Cact-dependent Dl diffusion and Dl binding to DNA. However, the biophysical parameters governing Dl nuclear dynamics that would support these claims have not been previously measured. In this work, the authors provide evidence that GFP-tagged Dl may be separated into a mobile pool and an immobile pool. Interestingly, the fraction of immobile Dl is position-dependent along the DV axis, revealing more binding to DNA in the ventral than in the dorsal nuclei. This is either due to higher binding affinity in ventral locations (due to Toll-dependent Dl phosphorylation) or to higher Dl-Cact binding in dorsal nuclei that would prevent Dl from binding to DNA. Using dl-mutant alleles, the authors support the latter hypothesis.

Strengths:

The manuscript is well written and their conclusions are convincingly supported by their methodology and analysis. As a quantitative study, the biophysical analysis seems rigorous, in general.

Although this is not the first study that employs FSC to investigate the dynamics of a morphogen, it further exemplifies how these quantitative tools can be used to uncover mechanistic aspects of morphogen dynamics during development. In particular, the manuscript reports novel biophysical parameters of Dl dynamics that will be helpful in future hypotheses-driven modeling studies.

Weaknesses:

In my opinion, the main weakness of the manuscript is that the main biological implication of the study, namely that the asymmetry in the fraction of immobile Dl is a result of nuclear Dl-Cact binding which prevents Dl from binding DNA (Figure 5), occurs in a region of the embryo where there is very little Dl anyways (Figure 1A, 5A). While it is interesting that the fraction of immobile Dl increases (just a little, but significantly) in dorsal nuclei in mutants expressing a form of Dl with reduced Cact binding it is unclear what is the biological impact of this effect in a location where Dl is nearly absent. As can be seen in Figure 3F, the fraction of immobile is unaffected in Dl-mutant forms with reduced DNA binding, because it is already very low. It is unlikely that Dl binding to Cact in dorsal nuclei would affect shuttling as well since the fraction is very low anyway.

While the authors have a very clear understanding of the biology of the Dl gradient, I feel that the manuscript is more written as a 'tools' paper (i.e., to exemplify how FSC methods and analysis can be used for biological discovery). This is ok, but I think that the authors should discuss further what are the biological implications of these findings other than the contribution to uncovering the biophysical parameters. For example, I think that the implications of the rejected hypothesis (i.e., that Toll-dependent Dl phosphorylation does not seem to have an impact on Dl binding affinities to DNA) are important and should be further discussed (even if no additional experiments are performed). What is then the role of Dl phosphorylation? Perhaps it could have an impact on patterning robustness in lateral regions. The authors should report in Figure 5 also what happens to the fraction of Dl bound to DNA in lateral regions in the reduced Cact binding and reduced Toll phosphorylation mutants.

The way that position along the DV axis is reported using the nuclear-cytoplasmic-ratio (NCR) in Figures 1-3 is not incorrect, but I wonder if it is the best way of doing it. The reason is that it spreads out a relatively small region of the embryo (the ventral-most locations) and shrinks a relatively large region of the embryo (lateral and dorsal regions), see Figure 1A. Perhaps reporting the NCR in log_2 units would be more appropriate.

eLife Assessment

With compelling electrophysiological and behavioural evidence, this work establishes that the activity of insulin-producing cells (IPCs) depends on the nutritional state in Drosophila and that, like in mammals, there is also an incretin-like effect with IPCs responding to glucose feeding but not to glucose perfusion. Moreover, the authors demonstrate that DH44 neurons respond to glucose perfusion and, together with IPCs, modulate locomotor activity. This important study on the neuronal regulation of metabolic homeostasis will be of interest to both neuroscience and to medical research in diabetes.

Reviewer #1 (Public review):

Summary:

This study presents useful insights into the in vivo dynamics of insulin-producing cells (IPCs), key cells regulating energy homeostasis across the animal kingdom. The authors further provide compelling evidence using adult Drosophila melanogaster that IPCs, unlike neighboring DH44 cells, do not respond to glucose directly, but that glucose can indirectly regulate IPC activity after ingestion supporting an incretin-like mechanism in flies similar to mammals. The authors link decreased activity of IPCs to hyperactivity observed in starved flies, a locomotive behavior aimed to increase food search. Furthermore, the authors provide evidence that IPCs receive inhibitory inputs from Dh44 neurons, which are linked to increased locomotor activity.

This paper is of outstanding interest to scientists aiming to understand metabolic control of circuit dynamics, in particular for internal state-linked behaviors competing with the feeding state.

Strengths:

(1) By using whole cell patch clamp recording, the authors convincingly showed the activity pattern and regulation of IPCs and neighboring DH44 neurons under different feeding states and in various refeeding paradigms.<br /> (2) The paper provides compelling evidence that IPCs are not directly and acutely activated by glucose, but rather through a post-ingestive incretin-like mechanism. In addition, the authors show that Dh44 neurons located adjacent to the IPCs respond to bath application of nutritive sugars contrary to the IPCs.<br /> (3) The paper also provides useful data on the regulation of IPC activity by Dh44 neurons, which is useful to understand their regulation in vivo.

No major weaknesses remain in the revised version of this work.

Reviewer #2 (Public review):

Summary

In this study, Bisen et al. characterized the state-dependency of insulin-producing cells in the brain of Drosophila melanogaster. They successfully established that IPC activity is modulated by the nutritional state and age of the animal. Interestingly, they demonstrate that IPCs respond to the ingestion of glucose, rather than to perfusion with it, an observation reminiscent of the incretin effect in mammals. The study is well conducted and presented and the experimental data convincingly support the claims made.

Strengths

The study makes great use of the tools available in *Drosophila* research, demonstrating the effect that starvation and subsequent refeeding have on the physiological activity of IPCs as well as on the behavior of flies to then establish causal links by making use of optogenetic tools.<br /> It is particularly nice to see how the authors put their findings in context to published research and use for example TDC2 neuron activation or DH44 activity to establish baselines to relate their data to.

Reviewer #3 (Public review):

Although insulin release is essential in the control of metabolism, adjusted to nutritional state, and plays major roles in normal brain function as well as in aging and disease, our knowledge about the activity of insulin-producing (and releasing) cells (IPCs) in vivo in limited.

In this technically demanding study, IPC activity is studied in the Drosophila model system by fine in vivo patch clamp recordings with parallel behavioral analyses and various optogenetic as well as feeding manipulations.

The data provide compelling evidence that IPC activity is increased with a slow time course after feeding a high glucose diet. By contrast, IPC activity is not directly affected by rising blood glucose levels. This is reminiscent of the incretin effect known from vertebrates and points to a conserved mechanism in insulin production and release upon sugar feeding.

Moreover, the data confirm earlier studies that nutritional state strongly affects locomotion. Surprisingly, strong evidence shows that IPC activity makes only a negligible contribution to this. Instead, other modulatory neurons that are directly sensitive to blood glucose levels strongly affect locomotion. Together, these data reveal a network of multiple parallel and interacting neuronal layers to orchestrate the physiological, metabolic, and behavioral responses to the nutritional state. Together with the data from a previous study, this work sets the stage to dissect the architecture and function of this network.

Strengths:

State-of-the-art current clamp in situ patch clamp recordings in behaving animals are a demanding but powerful method to provide novel insight into the interplay of nutritional state, IPC activity, and locomotion. The patch clamp recordings and the parallel behavioral analyses are of high quality, as are the optogenetic manipulations. The data showing that starvation silences IPC activity in young flies (younger than 1 week) are excellent. The evidence for the claim that locomotor activity is not increased upon IPC activity but upon the activity of other blood glucose sensitive modulatory neurons (Dh44) is compelling, too. The study provides a great system to experimentally dissect the interplay of insulin production and release with metabolism, physiology, nutritional state, and behavior. Demonstrating the incretin effect in Drosophila provides novel experimental routes to further study it. During the revision process, compelling evidence has been added to underscore the incretin effect, the finding that IPCs themselves do not sense sugars, and that feeding a high sugar diet does not cause unspecific stress responses.

I found no more weaknesses: The authors have carefully addressed all of my previous critiques by adding compelling new data and carefully revising the text. This paper provides a prime example of how responsible authors can utilize this constructive (but relatively new) reviewing procedure to make a very good manuscript even better.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

This study presents useful insights into the in vivo dynamics of insulin-producing cells (IPCs), key cells regulating energy homeostasis across the animal kingdom. The authors provide compelling evidence using adult Drosophila melanogaster that IPCs, unlike neighboring DH44 cells, do not respond to glucose directly, but that glucose can indirectly regulate IPC activity after ingestion supporting an incretin-like mechanism in flies, similar to mammals. The authors link the decreased activity of IPCs to hyperactivity observed in starved flies, a locomotive behavior aimed at increasing food search. 

Furthermore, there is supporting evidence in the paper that IPCs receive inhibitory inputs from Dh44 neurons, which are linked to increased locomotor activity. However, although the electrophysiological data underlying the dynamics of IPCs in vivo is compelling, the link between IPCs and other potential elements of the circuitry (e.g. octopaminergic neurons) regulating locomotive behaviors is not clear and would benefit from more rigorous approaches. 

This paper is of interest to cell biologists and electrophysiologists, and in particular to scientists aiming to understand circuit dynamics pertaining to internal state-linked behaviors competing with the feeding state, shown here to be primarily controlled by the IPCs. 

Strengths: 

(1) By using whole-cell patch clamp recording, the authors convincingly showed the activity pattern of IPCs and neighboring DH44 neurons under different feeding states. 

(2) The paper provides compelling evidence that IPCs are not directly and acutely activated by glucose, but rather through a post-ingestive incretin-like mechanism. In addition, the authors show that Dh44 neurons located adjacent to the IPCs respond to bath application of glucose contrary to the IPCs. 

(3) The paper provides useful data on the firing pattern of 2 key cell populations regulating foodrelated brain function and behavior, IPCs and Dh44 neurons, results which are useful to understand their in vivo function. 

Weaknesses: 

(1) The term nutritional state generally refers to the nutrients which are beneficial to the animal. In Figure 1, the authors showed that IPCs respond to glucose but not proteins. To validate the term nutritional state the authors could test the effect of a non-nutritive sugar (e.g. D-arabinose or L-Glucose) on the post-ingestive physiological responses of the IPCs.

We thank the referee for this insightful comment. Following their suggestion, we included two new experimental data sets, which we added to Figure 1: We show that IPCs do not respond to the non-nutritive sugar D-arabinose (Figure 1H). In order to further expand this data set and our conclusions, we additionally show that IPCs do respond to fructose – a second nutritive sugar in addition to glucose (Figure 1H). Together, these data sets permit the conclusion that IPCs are sensitive to the ingestion of nutritive sugars, and do not respond to ingestion of nonnutritive sugars or high protein diets. Thus, we validate the term nutritional state.

(2) It is difficult to grasp the main message from the figures in the result section as some figures have several results subsections referring to different points the authors want to make. The key results of a figure will be easier to understand if they are summarized in one section of the results. Alternatively, a figure can be split into 2 figures if there are several key messages in those figures, e.g. Figures 2 and 3.  

We appreciate this suggestion and have made several changes to our manuscript to add more clarity. Among other things, we have changed the order of data presentation in Figure 2, as suggested by the referee below, where we now start with the IPC activation data rather than the OAN activation. We also swapped the order of data presentation and split Figure S1 into Figures S1 & S2. Moreover, we re-arranged the panel order in supplementary figure S4. This significantly improved the flow of the results section. Since the figures the referee refers to contain comparative data, for example between diets (Figure 1) or neuron types (Figure 2), we prefer to keep these data sets together. However, we have carefully revised the results section to more clearly relate our statements to individual figure panels.

(3) The prime investigation of the paper is about the physiological response and locomotive behavioral readout linked to IPCs. The authors do not show a link between OANs and IPCs in terms of functional or behavioral readouts. In Figure 2 the authors first start with stating a link between OAN neurons and locomotion changes resulting from internal feeding states. The flow of the paper would be better if the authors focused on the effect of optogenetic activation of IPCs under different feeding states and their impact on fly locomotion. If the experiments done on optogenetic activation of OANs were to validate the experimental approach the data on OAN neurons is better suited for the supplement without the need of a subsection in the result section on the OANs.  

We agree with the reviewer’s suggestion and switched the order of the figure panels and text to aid the flow of the manuscript. We now show and discuss the IPC activation data first (Figure 2C-H) and OAN activation afterwards (Figure 2I-K). We did keep the OAN data in the main document, though, since that facilitates comparisons between the small effects of IPC activation and the large, well-established effects of OAN activation.

(4) Figure 2F shows that optogenetic activation of IPCs in fed flies does not influence their locomotor output. In the text, the conclusion linked to Figure 2F-H states that IPC activation reduces starvation-induced hyperactivity which is a statement more suited to Figure 2I-K. 

We edited the text accordingly.

(5) The authors show activation of Dh44 neurons leads to hyperpolarisation of the IPCs. What is the functional link between non-PI Dh44 neurons and the IPCs? Do IPCs express DH44R or is DH44 required for this effect on IPCs? Investigating a potential synaptic or peptidergic link between DH44 neurons and IPCs and its effect on behavior would benefit the paper, as it is so far not well connected. 

Although we have not performed any experiments dedicated to investigating the functional link between DH44Ns outside the PI and the IPCs in this study, there are two lines of evidence supporting that this connection is relatively direct. First, IPCs do express DH44R1 & R2, as we show in a parallel study in eLife (Held M, et al. ‘Aminergic and peptidergic modulation of Insulin-Producing Cells in Drosophila’. eLife. 2024;13. doi:10.7554/ELIFE.99548.1). Second, we performed functional connectivity experiments using a Leucokinin (LK) driver line in that paper. This driver line labels two pairs of non-PI DH44Ns in the VNC, which are DH44 and LK positive (Zandawala et al 2018). Activating that line leads to inhibition of IPCs, similar to the effect we observed here for DH44N activation. These two lines of evidence suggest that there could be a direct peptidergic connection between DH44+ neurons and IPCs. We have added a paragraph mentioning these experiments to our discussion:

‘Notably, the DH44^PINs express the DH44 peptide, as confirmed by anti-DH44 stainings(100). This also applies to a large fraction of neurons labelled in the broad DH44 driver line(100). However, a subset of neurons labelled in the broad line did not exhibit DH44 immunoreactivity(100), and might therefore not actually express the DH44 peptide. Hence, the inhibition of IPCs could be driven by neurons in the DH44 driver line that do not express DH44. A strong candidate for the inhibition are LK and DH44-positive neurons, which are labelled by the broad line(76). In a parallel study, we showed that LK-expressing neurons strongly inhibit IPCs(30), similar to the broad DH44 line used here. Furthermore, evidence from single-nucleus transcriptomic analysis shows that IPCs express DH44-R1 and DH44-R2 receptors(30). Therefore, it is possible that DH44Ns communicate with IPCs through a direct peptidergic connection. Notably, the inhibitory effect of non-PI DH44Ns on IPCs was very strong and fast, suggesting that a connection via classical synapses is more likely. Regardless, our results show that the glucose sensing DH44^PINs and IPCs act independently of each other.’

Reviewer #2 (Public Review): 

Summary: 

In this study, Bisen et al. characterized the state-dependency of insulin-producing cells in the brain of *Drosophila melanogaster*. They successfully established that IPC activity is modulated by the nutritional state and age of the animal. Interestingly, they demonstrate that IPCs respond to the ingestion of glucose, rather than to perfusion with it, an observation reminiscent of the incretin effect in mammals. The study is well conducted and presented and the experimental data convincingly support the claims made. 

Strengths: 

The study makes great use of the tools available in *Drosophila* research, demonstrating the effect that starvation and subsequent refeeding have on the physiological activity of IPCs as well as on the behavior of flies to then establish causal links by making use of optogenetic tools. 

It is particularly nice to see how the authors put their findings in context to published research and use for example TDC2 neuron activation or DH44 activity to establish baselines to relate their data to. 

Weaknesses: 

I find the inability of SD to rescue the IPC starvation effect in Figure 1G&H surprising, given that the fully fed flies were raised and kept on that exact diet. Did the authors try to refeed flies with SD for longer than 24 hours? I understand that at some point the age effect would also kick in and counteract potential IPC activity rescue. I think the manuscript would benefit if the authors could indicate the exact age of the SD refed flies and expand a bit on the discussion of that point.  

We have expanded the first paragraph of our discussion to tackle these questions, in particular the potential effect of aging, as suggested by the referee. We now also indicate the exact age of the flies. Moreover, we have conducted additional experiments in which we added either glucose or arabinose to our standard diet (Figure 1H). As we would have expected based on our hypothesis that the glucose concentration in our standard diet was too low to cause an increase in IPC activity after starvation, we find that feeding standard diet plus glucose increases IPC activity to the same level as glucose only, and that adding arabinose to the standard diet does not lead to increased IPC activity after starvation (Figure 1H).

The incretin-like effect is exciting and it will be interesting in the future to find out what might be the signal mediating this effect. It is interesting that IPCs in explants seem to be responsive to glucose. I think it would help if the authors could briefly discuss possible sources for the different findings between these in fact very different preparations. Could the the absence of the inhibitory DH44 feedback in the *ex-vivo* recordings for example play a role? 

We thank the referee for this interesting point and expanded our discussion accordingly. We included that, in particular in brain explants without a VNC, the inhibitory connection we describe might be absent, as the referee suggested: ‘Previous ex vivo studies suggested that IPCs, like pancreatic beta cells, sense glucose cell-autonomously(23,24). Consistent with this, we observed an increase in IPC activity after the ingestion of glucose (Figure 2B). However, IPC activity did not increase during the perfusion of glucose directly over the brain. Importantly, the fly preparations were kept alive for several hours allowing the glucose-rich saline to enter circulation and reach all body parts. Several factors may explain the difference between ex vivo and in vivo preparations. First, in ex vivo studies, certain regulatory feedback mechanisms present in vivo could be absent. For example, the strong inhibitory input IPCs receive from DH44Ns we found would likely be absent in brain explants without a VNC. A lack of inhibitory feedback might allow for more direct glucose sensing by IPCs ex vivo, whereas in vivo, the IPC response could be suppressed by more complex systemic feedback. Second, we attempted to use the intracellular saline formulation employed in a previous ex vivo study44. However, we observed that IPCs depolarized quickly using this saline, leading to unstable recordings that did not meet our quality standards for in vivo experiments. Another possible explanation for the lack of an effect of glucose might have been that the dominant circulating sugar in flies is trehalose(70,71) which is derived from glucose. When we extended our experiments, we found that trehalose perfusion did not affect IPC activity either, strengthening the idea that IPCs do not directly sense changes in hemolymph sugar levels. Therefore, our findings suggest that, similar to mammals, IPC activity and hence, insulin release, is not simply modulated by hemolymph sugar concentration in Drosophila.’ 

The incretin-like effect the authors observed seems to start only after 5h which seems longer than in mammals where, as far as I know, insulin peaks around 1h. Do the authors have ideas on how this timescale relates to ingestion and glucose dynamics in flies? 

We have now included the following section in the discussion to explicitly address the question of different activity dynamics in flies and mammals, but also the limitations of our electrophysiological approach in this regard: ‘We observed that IPC activity increased over a timescale of hours, which is longer compared to the fast insulin response in mammals, where insulin typically peaks within an hour of feeding(97). In flies, insulin levels rise within minutes of refeeding, followed by a drop after 30 min(20). Our experimental techniques limit our ability to capture these fast initial dynamics, since the preparation for intracellular recordings requires tens of minutes, so that we typically recorded IPC activity at least 20 min after the last food ingestion. Notably, studies in fasted mammals have shown that insulin peaks within minutes of refeeding, followed by a rapid decline, with levels stabilizing as feeding continues(98,99). We speculate a similar dynamic could be present in flies, but with our approach, we capture the steady-state reached tens of minutes after food ingestion rather than a potential initial peak.’ 

The authors mention "a decrease in the FV of IPC-activated starved flies even before the first optogenetic stimulation (Figure 2I),". Could this be addressed by running an experiment in darkness, only using the IR illumination of their behavioral assay? 

We thank the referee for pointing out this unexpected result. We discuss this in more detail in the new version of our manuscript and expand on the reasons for not performing these optogenetic activation experiments in the dark: First, the red LED required to activate CsChrimson triggers strong startle responses in dark-adapted flies, which mask other behavioral effects, in particular subtle ones such as those observed for IPCs. The startle response is much reduced when performing experiments under low background light conditions. Second, flies, at least in our hands, do not exhibit robust foraging behavior or starvation-induced hyperactivity in the dark, which is critical for our behavioral experiments. However, we also explain in our discussion that we believe the effect of background illumination is relatively small, since flies expressing CsChrimson in OANs or DH44Ns show comparable activity levels to controls. Hence, a part of this effect is likely attributable to leak currents induced by CsChrimson expression. We would like to point out though that we are careful in our description of the IPC effect on behavior, and focus on the fact that it is considerably smaller than the effects of other modulatory neurons (DH44Ns and OANs).

The authors show an inhibitory effect of DH44 neuron activation on IPC activity. They further demonstrate that DH44PI neurons are not the ones driving this and thus conclude that "...IPCs are inhibited by DH44Ns outside the PI.". As the authors mentioned the broad expression of the DH44-Gal4 line, can they be sure that the cells labeled outside the PI are actually DH44+? If so they should state this more clearly, if not they should adapt the discussion accordingly.   

We have substantially added to our discussion of this point, according to the referee’s great suggestion. In short, the broad line includes neurons that are DH44 positive and neurons that are not: ‘Notably, the DH44^PINs express the DH44 peptide, as confirmed by anti-DH44 stainings(100). This also applies to a large fraction of neurons labelled in the broad DH44 driver line(100). However, a subset of neurons labelled in the broad line did not exhibit DH44 immunoreactivity(100), and might therefore not actually express the DH44 peptide. Hence, the inhibition of IPCs could be driven by neurons in the DH44 driver line that do not express DH44.’

Reviewer #3 (Public Review): 

Although insulin release is essential in the control of metabolism, adjusted to nutritional state, and plays major roles in normal brain function as well as in aging and disease, our knowledge about the activity of insulin-producing (and releasing) cells (IPCs) in vivo is limited. 

In this technically demanding study, IPC activity is studied in the Drosophila model system by fine in vivo patch clamp recordings with parallel behavioral analyses and optogenetic manipulation. 

The data indicate that IPC activity is increased with a slow time course after feeding a high-glucose diet. By contrast, IPC activity is not directly affected by increasing blood glucose levels. This is reminiscent of the incretin effect known from vertebrates and points to a conserved mechanism in insulin production and release upon sugar feeding. 

Moreover, the data confirm earlier studies that nutritional state strongly affects locomotion. Surprisingly, IPC activity makes only a negligible contribution to this. Instead, other modulatory neurons that are directly sensitive to blood glucose levels strongly affect modulation. Together, these data indicate a network of multiple parallel and interacting neuronal layers to orchestrate the physiological, metabolic, and behavioral responses to nutritional state. Together with the data from a previous study, this work sets the stage to dissect the architecture and function of this network. 

Strengths: 

State-of-the-art current clamp in situ patch clamp recordings in behaving animals are a demanding but powerful method to provide novel insight into the interplay of nutritional state, IPC activity, and locomotion. The patch clamp recordings and the parallel behavioral analyses are of high quality, as are the optogenetic manipulations. The data showing that starvation silences IPC activity in young flies (younger than 1 week) are compelling. The evidence for the claim that locomotor activity is not increased upon IPC activity but upon the activity of other blood glucose-sensitive modulatory neurons (Dh44) is strong. The study provides a great system to experimentally dissect the interplay of insulin production and release with metabolism, physiology, and behavior. 

Weaknesses: 

Neither the mechanisms underlying the incretin effect, nor the network to orchestrate physiological, metabolic, and behavioral responses to nutritional state have been fully uncovered. Without additional controls, some of the conclusions would require significant downtoning. Controls are required to exclude the possibility that IPCs sense other blood sugars than glucose. The claim that IPC activity is controlled by the nutritional state would require that starvation-induced IPC silencing in young animals can be recovered by feeding a normal diet. At current firing in starvation, silenced IPCs can only be induced by feeding a high-glucose diet that lacks other important ingredients and reduces vitality. Therefore, feasible controls are needed to exclude that diet-induced increases in IPC firing rate are caused by stress rather than nutritional changes in normal ranges. The finding that refeeding starved flies with a standard diet had no effect on IPC activity but a strong effect on the locomotor activity of starved flies contradicts the statement that locomotor activity is affected by the same dietary manipulations that affect IPC activity. The compelling finding that starvation induces IPC firing would benefit from determining the time course of the effect. The finding that IPCs are not active in fed animals older than 1 week is surprising and should be further validated. 

We thank the referee for the thoughtful and constructive criticism of our experiments and conclusions. Below, we lay out how we tackled the individual points raised by the referee.

(1) ‘Controls are required to exclude the possibility that IPCs sense other blood sugars than glucose.’  

To address this point, we conducted experiments in which we perfused trehalose (Figure 3B), the main circulating hemolymph sugar in Drosophila and other insects. Our results clearly show that trehalose does not affect IPC activity upon perfusion, confirming our statements that IPCs do not sense key blood sugars directly.

(2) ‘Feasible controls are needed to exclude that diet-induced increases in IPC firing rate are caused by stress rather than nutritional changes in normal ranges’. 

We agree with the referee that this point was not completely fleshed out in our first submission. We have now performed additional experiments in which we added glucose (and fructose) to our standard diet (Figure 1H). Flies feeding on this diet received all necessary nutrients but still experienced high concentrations of sugars. The effects of high glucose in a standard diet background were indistinguishable from those of high glucose in agarose, confirming that the IPCs respond to sugar rather than stress. Another important observation in this context is that IPCs in flies kept on a high protein diet exhibited much lower spike rates than flies exhibiting the high glucose diet, even though they had a much shorter lifespan and therefore, presumably, experienced much higher stress levels (Figure 1H, Figure S1). These observations underline that stress is certainly not the primary factor here.

(3) ‘The finding that refeeding starved flies with a standard diet had no effect on IPC activity but a strong effect on the locomotor activity of starved flies contradicts the statement that locomotor activity is affected by the same dietary manipulations that affect IPC activity.’

We have revised the respective section of the results and discussion accordingly and are more careful and clearer in our interpretation of this behavioral dataset: ‘These results show that the locomotor activity was affected by the same dietary manipulations that had strong effects on IPC activity. However, IPC activity changes alone cannot explain the modulation of starvation-induced hyperactivity. On the one hand, high-glucose diets which drove the highest activity in IPCs were not sufficient to reduce locomotor activity back to baseline levels. On the other hand, refeeding flies with SD did not revert the effects of starvation on IPC activity (Figure 1H), but it was sufficient to reduce the locomotor activity below baseline levels (Figure 2B). This suggests that the modulation of starvation-induced hyperactivity is achieved by multiple modulatory systems acting in parallel.’

(4) ‘The compelling finding that starvation induces IPC firing would benefit from determining the time course of the effect.’

We followed the referee’s excellent suggestion and determined the time course of the starvation effect in three timesteps, similar to the experiments we did for refeeding (Figure 1G). In addition, we now also quantify the number of active IPCs (i.e., IPCs that fired at least one action potential during our five-minute analysis window), which further illustrates the dynamics of the starvation and refeeding effects. We find that the starvation effect is graded, and that IPC activity decreases with increasing starvation duration.

(5) ‘The finding that IPCs are not active in fed animals older than 1 week is surprising and should be further validated.’

To address the referee’s comment, we have added 14 new IPC recordings from flies in the 6–26-day range, such that we now have recordings from 9-14 IPCs for each age range (Figure S2B). They confirmed our previous analysis and strengthened the finding that IPC activity dramatically decreases after 8 days (on our standard diet). The total number of IPCs in this supplementary dataset was thus increased from 34 to 48.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

(1) Do IPCs respond to glucose specifically after ingestion or generally to any other nutritive sugars? To tackle this question the IPC responses in starved flies can be recorded after refeeding flies with other nutritive sugars (fructose, sucrose). 

To address this important question, we have performed additional experiments in which we refed starved flies with fructose, as a nutritive sugar, and arabinose, as a non-nutritive sugar. As expected, IPCs responded to fructose but not arabinose and hence nutritive sugars in general. We describe and discuss these key results in the new version of our manuscript.

(2) In Figure 2, the x and y axes are not annotated on all subfigures, which might help improve clarity. 

We have annotated the subfigures as requested.

(3) In the discussion on page 9 ("...we observed an increase in IPC activity after the ingestion of glucose (Figure 2B)."), the authors refer to Figure 2B instead of 3C.

We have fixed this oversight.

Reviewer #2 (Recommendations For The Authors): 

Introduction 

I think it could be helpful for the reader if you would briefly state the number of IPCs and whether you are targeting all of them with Dilp2-Gal4. 

We included the numbers according to the suggestion. 14 IPCs are labeled in the driver line, and this is the number of IPCs commonly assumed to be present in the PI.

Figures 

In some Figures (for example 1D & E) the authors state the number of IPCs recorded (N) but not the number of animals used (n). This should be stated as the data from within an animal are dependent and might give insights about IPC heterogeneity. 

We have compiled tables for the supplementary material (Tables S5 & S6) in which we state the number of IPCs and DH44^PINs recorded and the number of different flies for each figure panel. We have recorded an average of 1.4 IPCs per fly (217 IPCs from 160 flies). We therefore expect the bias introduced by individual flies to be rather small. However, in our parallel study, we specifically investigate the heterogeneity of IPCs by maximizing the number of IPCs recorded per fly (Held M, et al. ‘Aminergic and peptidergic modulation of Insulin-Producing Cells in Drosophila’. eLife. 2024;13. doi:10.7554/ELIFE.99548.1). In the case of DH44PINs, we recorded 24 neurons in 21 flies – 1.1 neurons per fly.

- Figure 3D: There is some white visible among the cell bodies in the overlay. I assume this comes from projecting across layers rather than indicating DH44 - IPC overlap? It would help to explicitly state that. 

We have added a statement to the results section, in which we explain that most of the white is due to overlap in the z-projection rather than overlap in the driver lines. However, there are few cases (typically one to two cells per brain), in which neurons labeled by the DH44 line also stain positive for Dilp2, indicating they express both neuropeptides. We have added this information to the manuscript:  

Results: ‘DH44^PINs are anatomically similar to IPCs, and their cell bodies are located directly adjacent to those of IPCs in the PI, making them an ideal positive control for our experiments (Figure 3D). A small subset of DH44^PINs also expresses Dilp2(75), and our immunostainings confirmed colocalization of Dilp2 and DH44 in a single neuron (Figure 3D, white arrow).’

In figure caption: ‘UAS-myr-GFP was expressed under a DH44-GAL4 driver to label DH44 neurons. GFP was enhanced with anti-GFP (green), brain neuropils were stained with anti-nc82 (cyan), and IPCs were labelled using a Dilp2 antibody (magenta). White arrow indicates Dilp2 and DH44-GAL4 positive neuron. The other white regions in the image result from an overlap in z-projections between the two channels, rather than from antibody colocalization.’

- Figure 4I: One might get the impression that the fast onset peak of activity precedes the stimulation onset, using a thinner line width might help avoid that. 

This effect is due to a combination of using relatively heavy lines for clear visibility of the data and a gentle smoothing step (a 2s median filter, which corresponds to less than 1% of the 300s stimulation window) in our analysis of the behavioral data. However, inspection of the raw data clearly shows increases in velocity after the onset of the optogenetic activation. We clarified this in the figure caption: ‘Average FV across all DH44N activation trials based on two independent replications of the experiment in I. Note that the peak in average FV lies within the first frame of the stimulation window.’

- S3 panel letters do not match references in the text.

We fixed this oversight.

Formatting 

- Page 10: The paragraphs on the bottom of the page got switched around.

This has been fixed.

- Page 14: The first paragraph after the header "Free-walking assay" seems to be coming from elsewhere. 

We apologize for this slightly embarrassing mistake. We used our related bioRxiv preprint (Held et al.) as a template for formatting this paper, and accidentally left this part of the methods section in the manuscript. We have fixed this error in our resubmission.

Reviewer #3 (Recommendations For The Authors): 

Major suggestions: 

(1) The data show convincingly that IPC activity is decreased by starvation during the first week of adult life (Figures 1C and D). However, the conclusion that IPC activity is controlled by the nutritional state requires additional care. First, refeeding starved adult animals with a normal diet does not bring back normal IPC firing rates (Figure 1H). Therefore, IPC activity does not strictly follow changes in nutritional state, but IPCs are silenced by starvation. Second, from the second week of adult life on, IPCs are silent anyway, and thus unlikely responsive to changes in the nutritional state anymore (which might be different on a different standard diet?) The only effect of feeding on IPC activity is observed upon feeding starved, young animals with high glucose for 12-24 hrs (Figure 1G). However, it is not clear whether increased IPC firing is caused by the effects of high glucose on the nutritional state in a normal range, or because of diet-induced stress (the diet also severely shortens lifespan, Figure 1S). Does high glucose also increase IPC firing rate in young, fed animals? These would have strongly increased glucose concentrations but not suffer the stress of not getting any other nutrients. Such experiments would be required to make the statement that glucose feeding increases IPC firing rate. 

We have performed several experiments to address this criticism. First, we performed a time course analysis of the starvation effect. We show that the IPC activity reduction is graded, and that IPC activity declines already after two hours of starvation, a timepoint at which stress levels should still be relatively small (Figure 1G). Second, we refed flies with high glucose concentrations added to the standard diet (Figure 1H). This minimized any potential stress responses due to a lack in nutrients. Third, we now show that IPCs specifically respond to nutritive (glucose and fructose), but not to non-nutritive sugars (arabinose, Figure 1H). We believe that these data sets, in addition to the graded refeeding effect, make a strong case for the nutritional state dependent modulation of IPCs. 

(2) The testing of locomotor activity is well done, nicely recapitulates starvation-induced increases in locomotion, and adds interesting novel findings on refeeding with high glucose versus high protein diet. However, the statement that locomotor activity was affected by the same dietary manipulations that had strong effects on IPC activity does not reflect the data presented. Refeeding starved flies with a standard diet had no effect on IPC activity (Figure 1H) but a strong effect on locomotor activity of starved flies (a strong reduction, even stronger than high glucose diet, Figure 2B). 

We have revised the respective section of the results and discussion accordingly and are more careful and clearer in our interpretation of this behavioral dataset: ‘These results show that the locomotor activity was affected by the same dietary manipulations that had strong effects on IPC activity. However, IPC activity changes alone cannot explain the modulation of starvationinduced hyperactivity. On the one hand, high-glucose diets which drove the highest activity in IPCs were not sufficient to reduce locomotor activity back to baseline levels. On the other hand, refeeding flies with SD did not revert the effects of starvation on IPC activity (Figure 1H), but it was sufficient to reduce the locomotor activity below baseline levels (Figure 2B). This suggests that the modulation of starvation-induced hyperactivity is achieved by multiple modulatory systems acting in parallel.’

Related to points 1 and 2, a key statement that the results establish that IPC activity is controlled by the nutritional state requires care. What the data convincingly show is that IPC activity is near zero upon starvation. 

As described above, we have added several extensive data sets (fructose feeding, arabinose feeding, trehalose perfusion, starvation time course) to show that we indeed observe a nutritional state dependent modulation of IPCs and describe these new results in the results and discussion.

(3) The time course of nutritional state-dependent changes of IPC activity is claimed to be slow, several hours to days. Unless I have missed a figure, the underlying data are not presented (only for high glucose diet). It would be great if this could also be shown for a standard diet with higher glucose concentrations than the one used so that it rescues starvation-induced IPC silencing without shortening lifespan (if this is feasible?). The data showing starvation-induced IPC silencing are convincing, but, unless I have missed it, the time course has not been determined. It would be very nice to actually show this. Have different starvation times been tested in relation to IPC firing rate, and if yes, with what time resolution? Does IPC activity change already after 0.5 or 1 or a few hours of starvation? If starvation can silence IPCs faster than assumed, the nearzero IPC activity in animals older than a week could very well be caused by longer time intervals between meals. 

We have performed experiments to address both important points raised by the referee here. 1) We have added high glucose concentrations to our standard diet, and show that it has the same effect – a significant increase in IPC activity – as the high glucose diet (Figure 1H). 2) We have analyzed the time course of IPC activity reduction in response to starvation (Figure 1G). Indeed, we find that a few hours of starvation start reducing IPC activity. We discuss the possibility that reduced IPC activity in older flies could be due to reduced food intake: ‘One of our experiments demonstrated that IPC activity was heavily diminished in flies older than 10 days (Figure S2B). A possible explanation could be that flies feed less as they age. However, this only holds true for flies older than 14 days86. Therefore, reduced IPC activity in 10-11 day old flies is unlikely to result from reduced food intake and likely involves inhibition of insulin signaling.’

(4) The data on the proposed incretin effect are of high importance in potentially highlighting a highly conserved link between glucose ingestion and insulin release. An important control would be to test different sugars, such as trehalose, an important blood sugar of flies. If glucose is converted into trehalose and this is what IPCs sense, then perfusion of glucose has no effect. The fact fantastic experiments show that the DH44 neurons are sensitive to glucose perfusion does rule out that IPCs sense a different sugar. This would be very different from the incretin effect that requires additional hormones. In addition, as mentioned above, controls are required to show that high glucose affects IPCs as a nutrient and not as a stressor (see point 1), for example refeeding with a standard diet that contains a higher glucose concentration but does not reduce lifespan. Another great control to solidify the exciting claim on the incretin effect would be to knock out candidate Drosophila incretin hormones and test whether a high glucose diet stops increasing the IPC firing rate (although simpler controls might also do the job). 

We have performed the two key experiments suggested by the referee. 1) We perfused trehalose as the primary blood sugar of flies and showed that IPCs do not respond to trehalose perfusion (Figure 3B & C). This further strengthens the finding that IPC activity in flies shows an incretin-like effect. 2) We have added high concentrations of glucose to our standard diet to provide flies with a full diet that contains high glucose concentrations. IPC activity in these flies was indistinguishable from the activity in flies which consumed pure glucose diets. In contrast, IPC activity in flies kept on a high protein diet, which dramatically reduced lifespan, was very low. These results clearly show that higher IPC activity is not due to increased stress levels, but a function of nutritive sugar ingestion. We further validated this hypothesis by refeeding flies with fructose as a nutritive sugar, which increased IPC activity, and arabinose as a non-nutritive sugar, which did not affect IPC activity (Figure 1H).

Another point that might be relevant to this discussion is that IPC activity is almost entirely shut down during flight in Drosophila (which we showed in Liessem et al. 2023, Current Biology 33 (3), 449-463. e5). Several ‘stress hormones’ are released during flight, including octopamine. The fact that IPC activity is low in flying flies, starved flies, and flies kept on a pure protein diet (which all experience high stress levels), to us, very clearly suggests that stress is not the predominant factor here. We would also like to point out that, while the lifespan was reduced in flies kept on pure glucose diets, survival rates were at 100% until day 14, and we carried out our experiments on day 2 after starvation. Hence, these flies might not (yet) experience particularly high stress levels.

(5) The discussion relates the absence of IPC firing in animals older than 1 week to aging. However, given that the flies fed on a normal diet show the typical lifespan for Drosophila, a 10-dayold fly is still in its youth. Maybe flies at 10 days eat simply less and thus IPC spiking goes down as in starved flies, especially because the standard diet used contains low glucose. Do IPCs also become silent after a week if the animals are fed with a standard diet that contains a higher glucose concentration? Without additional controls, this part of the discussion is pretty speculative and should be revised. 

We agree with the reviewer, that it is not clear whether reduced IPC activity is a direct result of physiological changes that occur with aging, or an indirect effect of reduced food intake, which occur during aging. In both cases, in our view, it would be an age-related effect. Since this is a minor point of our manuscript, we decided not to perform additional experiments, other than significantly increasing the sample size for the aging data set already presented to shore up our findings (Figure S2B). We have, however, revisited the discussion of this point according to the referee’s suggestion: ‘One of our experiments demonstrated that IPC activity was heavily diminished in flies older than 10 days (Figure S2B). A possible explanation could be that flies feed less as they age. However, this only holds true for flies older than 14 days(85). Therefore, reduced IPC activity in 10-11 day old flies is unlikely to result from reduced food intake and likely involves inhibition of insulin signaling.’

Other suggestions: 

(6) For the mixed effects of octopamine and tyramine on larval locomotion that are referred to, it might be interesting to also look at Schützler et al 2019, PNAS because it shows that starvation activates TBH so that the octopamine to tyramine ratio is increased. 

We refer to Schützler et al. in the following paragraph of our discussion: ‘This intermittent locomotor arrest has been previously described in adult flies and is thought to be mediated by ventral unpaired median OANs, which have been suggested to suppress long-distance foraging behavior(69). Since these are not the only neurons we activate in the TDC2 line, we speculate that the stopping phenotype could also result from concerted effects of octopamine and tyramine modulating muscle contractions(65-67) and motor neuron excitability(68), as previously described in Drosophila larvae, or from OANs interfering with pattern generating networks in the ventral nerve cord (VNC) during longer activation(69).’  

(7) The reference list requires care. For example, reference 43 is identical to 67, reference 66 gives no information on incretin-like hormones in Drosophila as stated in the text 

We carefully double-checked our reference list and corrected the mistakes mentioned.

eLife Assessment

Although others have proposed that OHC electromotility subserves cochlear amplification by acting as a "fluid pump", and evidence for this has been found using electrical stimulation of excised cochleae, this fundamental study substantially advances our understanding of cochlear homeostasis. This is the first report to test the pumping effect in vivo and consider its implications for cochlear homeostasis and drug delivery. The manuscript provides compelling evidence for OHC-based fluid flow within the cochlea.

Reviewer #1 (Public review):

Summary:

The authors test the "OHC-fluid-pump" hypothesis by assaying the rates of kainic acid dispersal both in quiet and in cochleae stimulated by sounds of different levels and spectral content. The main result is that sound (and thus, presumably, OHC contractions and expansions) result in faster transport along the duct. OHC involvement is corroborated using salicylate, which yielded results similar to silence. Especially interesting is the fact that some stimuli (e.g., tones) seem to provide better/faster pumping than others (e.g., noise), ostensibly due to the phase profile of the resulting cochlear traveling-wave response.

Strengths:

The experiments appear well controlled and the results are novel and interesting. Some elegant cochlear modeling that includes coupling between the organ of Corti and the surrounding fluid as well as advective flow supports the proposed mechanism.

The current limitations and future directions of the study, including possible experimental tests, extensions of the modeling work, and practical applications to drug delivery, are thoughtfully discussed.

Reviewer #2 (Public review):

Although recent cochlear micromechanical measurements in living animals have shown that outer hair cells drive broadband vibration of the reticular lamina, the role of this vibration in cochlear fluid circulation remains unclear. The authors hypothesized that motile outer hair cells facilitate cochlear fluid circulation. To test this, they investigated the effects of acoustic stimuli and salicylate on kainic acid-induced changes in the cochlear nucleus activities. The results reveal that low-frequency tones accelerate the effect of kainic acid, while salicylate reduces the impact of acoustic stimuli, indicating that outer hair cells actively drive cochlear fluid circulation.

The major strengths of this study lie in its high significance and the synergistic use of both electrophysiological recording and computational modeling. Recent in vivo observations of the broadband reticular lamina vibration challenge the traditional view of frequency-specific cochlear amplification. Furthermore, there is currently no effective noninvasive method to deliver the drugs or genes to the cochlea. This study addresses these important questions by observing outer hair cells' roles in the cochlear transport of kainic acid. The author utilized a well-established electrophysiological method to produce valuable new data and a custom-developed computational model to enhanced the interpretation of their experimental results.

The authors successfully validated their hypothesis, showing through the experimental and modeling results that active outer hair cells enhance cochlear fluid circulation in the living cochlea.

These findings have significant implications for advancing our understanding of cochlear amplification and offer promising clinical applications for treating hearing loss by accelerating cochlear drug delivery.

Reviewer #3 (Public review):

Summary:

This study reveals that sound exposure enhances drug delivery to the cochlea through the non-selective action of outer hair cells. The efficiency of sound-facilitated drug delivery is reduced when outer hair cell motility is inhibited. Additionally, low-frequency tones were found to be more effective than broadband noise for targeting substances to the cochlear apex. Computational model simulations support these findings.

Strengths:

The study provides compelling evidence that the broad action of outer hair cells is crucial for cochlear fluid circulation, offering a novel perspective on their function beyond frequency-selective amplification. Furthermore, these results could offer potential strategies for targeting and optimizing drug delivery throughout the cochlear spiral.

Weaknesses:

The primary weakness of this paper lies in the surgical procedure used for drug administration through the round window. Opening the cochlea can alter intracochlear pressure and disrupt the traveling wave from sound, a key factor influencing outer hair cell activity. However, the authors do not provide sufficient details on how they managed this issue during surgery. Additionally, the introduction section needs further development to better explain the background and emphasize the significance of the work.

Author response:

The following is the authors’ response to the previous reviews.

Reviewer #1 (Public review):

Summary:

The authors test the "OHC-fluid-pump" hypothesis by assaying the rates of kainic acid dispersal both in quiet and in cochleae stimulated by sounds of different levels and spectral content. The main result is that sound (and thus, presumably, OHC contractions and expansions) result in faster transport along the duct. OHC involvement is corroborated using salicylate, which yielded results similar to silence. Especially interesting is the fact that some stimuli (e.g., tones) seem to provide better/faster pumping than others (e.g., noise), ostensibly due to the phase profile of the resulting cochlear traveling-wave response.

Strengths:

The experiments appear well controlled and the results are novel and interesting. Some elegant cochlear modeling that includes coupling between the organ of Corti and the surrounding fluid as well as advective flow supports the proposed mechanism.

The current limitations and future directions of the study, including possible experimental tests, extensions of the modeling work, and practical applications to drug delivery, are thoughtfully discussed.

Weaknesses:

Although the authors provide compelling evidence that OHC motility can usefully pump fluid, their claim (last sentence of the Abstract) that wideband OHC motility (i.e., motility in the "tail" region of the traveling wave) evolved for the purposes of circulating fluid---rather then emerging, say, as a happy by-product of OHC motility that evolved for other reasons---seems too strong.

We adjusted our tone to be less assertive.

Our measurements and simulations coherently suggest that active outer hair cells in the tail region of cochlear traveling waves drive cochlear fluid circulation.

Reviewer #2 (Public review):

Although recent cochlear micromechanical measurements in living animals have shown that outer hair cells drive broadband vibration of the reticular lamina, the role of this vibration in cochlear fluid circulation remains unknown. The authors hypothesized that motile outer hair cells may facilitate cochlear fluid circulation. To test this hypothesis, they investigated the effects of acoustic stimuli and salicylate, an outer hair cell motility blocker, on kainic acid-induced changes in the cochlear nucleus activities. The results demonstrated that acoustic stimuli reduced the latency of the kainic acid effect, with low-frequency tones being more effective than broadband noise. Salicylate reduced the effect of acoustic stimuli on kainic acid-induced changes. The authors also developed a computational model to provide a physical framework for interpreting experimental results. Their combined experimental and simulated results indicate that broadband outer hair cell action serves to drive cochlear fluid circulation.

The major strengths of this study lie in its high significance and the synergistic use of electrophysiological recording of the cochlear nucleus responses alongside computational modeling. Cochlear outer hair cells have long been believed to be responsible for the exceptional sensitivity, sharp tuning, and huge dynamic range of mammalian hearing. However, recent observations of the broadband reticular lamina vibration contradict widely accepted view of frequency-specific cochlear amplification. Furthermore, there is currently no effective noninvasive method to deliver the drugs or genes to the cochlea, a crucial need for treating sensorineural hearing loss, one of the most common auditory disorders. This study addresses these important questions by observing outer hair cells' roles in the cochlear transport of kainic acid. The well-established electrophysiological method used to record cochlear nucleus responses produced valuable new data, and the custom-developed developed computational model greatly enhanced the interpretation of the experimental results.

The authors successfully tested their hypothesis, with both the experimental and modeling results supporting the conclusion that active outer hair cells can enhance cochlear fluid circulation in the living cochlea.

The findings from this study can potentially be applied for treating sensorineural hearing loss and advance our understanding of how outer hair cells contribute to cochlear amplification and normal hearing.

Reviewer #3 (Public review):

Summary:

This study reveals that sound exposure enhances drug delivery to the cochlea through the nonselective action of outer hair cells. The efficiency of sound-facilitated drug delivery is reduced when outer hair cell motility is inhibited. Additionally, low-frequency tones were found to be more effective than broadband noise for targeting substances to the cochlear apex. Computational model simulations support these findings.

Strengths:

The study provides compelling evidence that the broad action of outer hair cells is crucial for cochlear fluid circulation, offering a novel perspective on their function beyond frequency-selective amplification. Furthermore, these results could offer potential strategies for targeting and optimizing drug delivery throughout the cochlear spiral.

Weaknesses:

The primary weakness of this paper lies in the surgical procedure used for drug administration through the round window. Opening the cochlea can alter intracochlear pressure and disrupt the traveling wave from sound, a key factor influencing outer hair cell activity. However, the authors do not provide sufficient details on how they managed this issue during surgery. Additionally, the introduction section needs further development to better explain the background and emphasize the significance of the work.

Comments on revisions:

Thank you for addressing the comments and concerns. The author has responded to all points thoroughly and clarified them well. However, please include the key points from the responses to the comments (Introduction ((3), (5)) and Results ((5)) into the manuscript. While the explanations in the response letter are reasonable, the current descriptions in the manuscript may limit the reader's understanding. Expanding on these points in the Introduction, Results, or Discussion sections would enhance clarity and comprehensiveness.

Introduction (3): As inner-ear fluid homeostasis is maintained locally, longitudinal electro-chemical gradients, including the endocochlear potential, may vary along the cochlear length (Schulte and Schmiedt 1992; Sadanaga and Morimitsu 1995; Hirose and Liberman 2003).

Introduction (5): We do not want to distract the readers from the primary message by discussing different drug delivery methods into the inner ear. This paper is regarding active outer hair cells’ new role as the title suggests. An extensive discussion of drug delivery can confuse the theme of this work.

Results (5): High frequencies were not tested because they would not affect drug delivery to the apex of the cochlea (i.e., the traveling waves stop near the CF location.)

eLife Assessment

The manuscript presents valuable findings of bone remodeling under chronic unpredictable mild stress (CUMS). This is an interesting work on mental stress on bone health and osteoporosis, and the authors offer solid evidence of decreased bone mass mediated by miR-335-3p/Fos signaling in osteoclasts that are involved in the induction of bone loss caused by CUMS. This revised version provided new data that improved the quality of the manuscript and addressed the reviewers' concerns.

Reviewer #1 (Public review):

I have reviewed the manuscript "Psychological stress disturbs bone metabolism via miR-335-3p/Fos signaling in osteoclast" with interest. The described findings are relevant and useful for daily practice in periodontology. The paper is concise, professionally written, and easy to read. In this study, Jiayao et al. revealed the role of miR-335-3p in psychological stress-induced osteoporosis. CUMS mice were constructed to observe the femur phenotype, osteoclasts were identified as the main research object, and miRNA-seq was used to find the key miRNAs linking the brain and peripheral tissues. This study showed that miR-335-3p expression was simultaneously reduced in murine NAC, serum, and bone under psychological stress. The miR-335-3p/Fos/NFATC1 signaling pathway was validated in osteoclasts to reveal the potential mechanism of enhanced osteoclast activity under psychological stress. This study, from a new perspective of miRNAs, indicates a possible cause of disturbed bone metabolism due to psychological stress and may suggest a new approach to treating osteoporosis.

Reviewer #2 (Public review):

Zhang et al. established chronic unpredictable mild stress (CUMS) mouse model, which displayed osteoporosis phenotype, suggesting a potential correlation between psychological stress and bone metabolism. They found that miRNA candidate miR-335-3p is downregulated in the long bone of CUMS mice through microRNA sequencing experiments and qRT-PCR. They further demonstrated that miR-335-3p attenuates osteoclast activity via inhibiting Fos signaling, which can induce NFATC1 expression and regulate osteoclast activity.

My concerns have been addressed. And the quality of the manuscript is improved significantly.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public Review):

I have reviewed, with interest, the manuscript "Psychological stress disturbs bone metabolism via miR-335-3p/Fos signaling in osteoclast". The described findings are relevant and useful for daily practice in periodontology. The paper is concise, professionally written, and easy to read. In this study, Jiayao et al. revealed the role of miR-335-3p in psychological stress-induced osteoporosis. CUMS mice were constructed to observe the femur phenotype, osteoclasts were identified as the primary research object, and miRNA-seq was used to find the key miRNAs linking the brain and peripheral tissues. This study showed that the expression of miR-335-3p was simultaneously reduced in mice's NAC, serum, and bone under psychological stress. The miR-335-3p/Fos/NFATC1 signaling pathway was validated in osteoclasts to reveal the potential mechanism of enhanced osteoclast activity under psychological stress. From a new perspective of miRNAs, this study indicates a possible cause of disturbed bone metabolism due to psychological stress and may suggest a new approach to treating osteoporosis.

We thank this reviewer for the instructive suggestions and encouragement.

Reviewer #2 (Public Review):

Zhang et al. established chronic unpredictable mild stress (CUMS) mouse model, which displayed osteoporosis phenotype, suggesting a potential correlation between psychological stress and bone metabolism. They found that miRNA candidate miR-335-3p is downregulated in the long bone of CUMS mice through microRNA sequencing and qRT-PCR experiments. They further demonstrated that miR-335-3p attenuates osteoclast activity via inhibiting Fos signaling, which can induce NFATC1 expression and regulate osteoclast activity.

Strengths:

The authors established CUMS mouse model and confirmed the osteoporosis phenotype through careful characterization of bone and analysis of osteoclast activity. They performed microRNA sequencing to identify the miRNA candidate regulating the bone loss in the CUMS mouse model. They also validated the expression of miR-335-3p and interfered with the function of miR-335-3p through an in vitro assay. Overall, the findings from this study provide important hints for the correlation between psychological stress and bone metabolism.

We thank this reviewer for the comprehensive summary and positive comment on our work.

Weakness:

The data provided by the authors are preliminary, especially the mechanistic insight, which needs to be enhanced. The authors have shown that miR-335-3p expression was altered in the CUMS mouse model and the change of its expression regulated osteoclast activity. The validation should be conducted in vivo, and the mechanism behind this should be investigated further.

We thank the reviewer’s important insight on the need for further in vivo validation of the role of miR-335-3p. Therefore, we designed and produced Antagomir-335-3p (antagonist) and Agomir-335-3p (agonist). Then, we injected them into the body through the tail vein for about 2 months and observed the bone phenotype in each group of mice. The results suggested that the decrease of miR-335-3p in vivo could lead to bone loss, which was consistent with our in vitro validation results (Figure 5H-I).

Reviewing Editor:

Method

(1) Bone histomorphometric analysis following ASBMR's guidelines Bone histomorphometric analysis of bone formation and bone resorption: The authors should follow ASBMR's guidelines for bone histomorphometry (PMCID: PMC3672237 and PMID: 3455637) to perform standard analyses of histomorphometry, rather than selected areas. They should also clearly describe a software used and define the areas analyzed.

We carefully re-analyzed bone histomorphometry according to ASBMR guidelines and combine this with our own understanding. At the same time, we improved the description of micro-CT and histological analysis in the method. If there is still any lack of standardization, we would be grateful for any constructive suggestions to improve this.

(2) Osteoclast cultures require nuclear staining to demonstrate multinucleated Trap positive cells.

We used the RAW264.7, a mouse macrophage-like cell line, for in vitro culture and induced its differentiation towards osteoclasts. Successfully induced osteoclasts showed enlarged cytoplasm and multinucleated fusion. Tartrate-resistant acid phosphatase (Trap) is the signature enzyme of osteoclasts. It can bind to the chromogen to exhibit a mauve color, based on the principle of azo-coupled immunohistochemistry. At the same time, small and rounded nuclei fused show a lighter color (author response image 1, yellow arrows). We attempted to stain the nuclei with hematoxylin based on this. However, it was unable to further distinguish the contours of the nuclei clearly due to the similar color to the Trap positive signals. Besides, many other scholars have assessed osteoclast activity in vitro experiments based solely on the results of Trap staining (area and number) (Cheng et al., 2022; Li et al., 2019; Ma et al., 2021; Zhong et al., 2023). Nevertheless, in the immunofluorescence staining of osteoclasts, the nuclei were labeled using a Hochest antibody to reflect the multinucleated fusion of osteoclasts (Figure 5G).  

(3) Osteoclast pit assays should be carried out to necessarily demonstrate the change of osteoclast resorption ability caused by miR-335-3p.

We added osteoclast pit assays to validate the role of miR-335-3p on osteoclast resorptive capacity (Figure 5D-E).

(4) Serum ELISA assay should be done to examine the global change of bone remodeling in the CUMS mice to assess bone formation and bone resorption that will support their claim.

We performed additional tests on serum concentrations of R-hydroxy glutamic acid protein (BGP), TRAP, Cathepsin K (CTSK), parathyroid hormone (PTH), calcium (CA), phosphate (P) in control and CUMS mice, which could better reflect the global change of bone remodeling in the CUMS mice (Figure 3— figure supplement 1).

(5) miR-RNA-seq: A labeled volcano plot should be used to replace the present one to show significant changes in differential gene expression.

We appreciate this great suggestion. We replaced the volcano plot that showed significant changes in differential gene expression (Figure 4B). We also uploaded the raw data to the GEO database (GSE253504), making the results clearer and more accessible.

Discussion

The authors should discuss previous works on the influences of hormones from the brain on chronic stress-induced bone loss and an association of these influences with their findings.

The discussion on the relationship between the bone metabolism regulation of both hormones and miR-335-3p in psychological stress was added in the second and fifth paragraphs of the discussion. To conclude, on the one hand, brain-derived and blood-transported miR-335-3p regulate bone metabolism synergistically. On the other hand, it exerted a more direct influence on bone under psychological stress.

Language

The language of the MS should be improved.

The manuscript has been carefully edited by a professional proofreader.

Reviewer #1 (Recommendations For The Authors):

(1) Figure 1F: The exact meaning of the Waveform Graph shown at left needs to be clarified for the not-so-experienced reader.

We added the more detailed meaning of the Waveform Graph in figure legends (Figure legend 1F).

(2) Is the concomitant increase in osteogenic and osteoblastic activity in this study consistent with that seen in similar disease studies? This could be added to the discussion.

In the fifth paragraph of the discussion section, we present the alterations of osteogenic and osteoblastic activity observed in other studies that are similar to ours. We also had a detailed discussion based on these observations.

(3) Figure 6A: Please highlight the key information to visualize the potential linkage among miR-335-3p, Fos, and osteoclast.

We highlighted the crucial linkage among miR-335-3p, Fos, and osteoclast with red arrows (Figure 6A)

4) Figure 6E: The specific area of the selected comparison needs to be clarified. Please add white dotted lines and lettering T (trabecular bone) and GP (growth plate) for the not-so-experienced reader. This will provide some orientation.

We used white dotted lines as well as letters to label the tissue in immunofluorescence staining images (Figure 6E).

(5) Line 350: "NAC derived and blood-trans, Ported miR-335-3p". There is a grammatical error. Please conduct general proofreading of the text and writing style.

Thank you for pointing this out. We have corrected this grammatical error, and we also checked the full text to correct similar errors.

Reviewer #2 (Recommendations For The Authors):

(1) miR-335-3p was downregulated in the femur in the CUMS mice. The possible mechanism for this outcome should be further discussed. In Figure 4B, the Volcano plot showed that only a few miRNA were differentially expressed between the control and CUMS mice. How do the authors explain this?

The chronic unpredictable mild stress (CUMS) model was constructed using normal mice. As the name of the model suggests, the stimulus is mild and does not cause developmental damage or teratogenic effects in mice. Conversely, CUMS has the potential to result in the chronic pathological conditions. Besides, in miRNA sequencing results from other tissues with similar models to ours, the number of differential miRNAs is also around a few dozen (Ma et al., 2019).

(2) The authors have demonstrated that miR-335-3p inhibits osteoclast differentiation based on an in vitro assay in Figure 5; however, an in vivo experiment is required to provide more solid evidence.

We strongly agree that in vivo experimental validation would bring more convincing results to this study. Therefore, we designed and produced Antagomir-335-3p (antagonist) and Agomir-335-3p (agonist), which were injected into mice via the tail vein every five days. Samples were collected at one and two months following the injection. We found that sustained two-month injections of antagomir could significantly lead to bone loss in mice (Figure 5H-I), which is consistent with our in vitro validation results.

However, the Agomir-miR-335-3p group did not exhibit a notable enhancement of bone mass. This may be attributed to the fact that the 11-week-old normal mice selected for this study were in their prime and did not have strong osteoclastic activity in vivo. Therefore, the osteoclastic inhibition of Agomir-335-3p could not be demonstrated.

In addition, no significant difference was seen one month after the injection. The main reason may be that the time is too short. On the one hand, the drug we injected was RNA preparation. They lacked stability resulting in poor delivery efficiency, which took some time to take effect. On the other hand, bone remodeling is also a time-consuming process.

(3) FOS and NFATC1 should be expressed in the nuclei of the cells, therefore, the quality of the images needs to be improved.

NFATC1 is a T-cell-activating nuclear factor that is activated in the nucleus to regulate the transcription of a variety of osteoclast-related genes, including ACP5, MMP9, etc. FOS could bind and interact with NFATC1, resulting in nuclear translocation and transcription activated. This could promote the differentiation and maturation of osteoclasts. They are both synthesized and processed in the cytoplasm and eventually enter the nucleus to perform their functions. Therefore, they are expressed in both the nucleus and the cytoplasm (Deng et al., 2022; Hounoki et al., 2008; Li et al., 2022).

In Figure 5G, we labeled cell nuclei with HOCHEST antibody with blue fluorescence, and more co-localized signals of nuclei (blue), FOS (red), and NFATC1 (green) were seen in the Inhibitor-miR-335-3p group, whereas the opposite result was observed in the Mimic-miR-335-3p group. These results indicated that inhibited miR-335-3p could promote osteoclast differentiation in vitro.

(4) The expression of FOS was elevated in CUMS group in Figure 6E; however, its mRNA level was unchanged, as shown in Figure 6 supplement; what is the explanation for this? How do the authors claim FOS is the downstream target if its mRNA expression is not impacted by CUMS?

The results demonstrated that miR-335-3p targeted binding to the mRNA of Fos did not result in mRNA degradation. Instead, this binding interferes with the protein translation process, which ultimately leads to the reduction of FOS protein.

(5) What would be the bone phenotype if a FOS inhibitor was injected into the control and CUMS mice? It is important to examine FOS function through an in vivo context.

The regulatory role of FOS for osteoclasts has been validated in numerous articles, both in vivo and in vitro(Aikawa et al., 2008; Cao et al., 2023; Cheng et al., 2022). For example, Aikawa et al. designed a small-molecule inhibitor of c-Fos/activator protein-1 (AP-1) using three-dimensional (3D) pharmacophore modeling, which helped verify the effect of FOS on osteoclasts in vivo(Aikawa et al., 2008).

We also strongly agree that in vivo injection of inhibitors of FOS, especially in CUMS mice, could further substantiate the role of miR-335-3p in osteoclasts under psychological stress. However, the study was constrained by the unavailability of commercially viable, efficacious small molecule inhibitors of FOS. In the future, we plan to design more precise therapeutic targets for psychological stress induced osteoporosis based on existing research ideas.

Reference

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eLife Assessment

This study presents an advance in efforts to use histone post-translational modification (PTM) data to model gene expression and predict epigenetic editing activity. Such models are broadly useful to the research community, especially ones that can model epigenetic editing activity, which is novel; additionally, the authors have nicely integrated datasets across cell types into their model. The work is mostly solid, but it would be strengthened by performing rigorous comparisons to existing methods that predict gene expression from PTM data and from additional model validation beyond dCas9-p300 based perturbations.

Reviewer #1 (Public review):

Batra, Cabrera and Spence et al. present a model which integrates histone posttranslational modification (PTM) data across cell models to predict gene expression with the goal of using this model to better understand epigenetic editing. This gene expression prediction model approach is useful if a) it predicts gene expression in specific cell lines b) it predicts expression values rather than a rank or bin, c) if it helps us to better understand the biology of gene expression or d) it helps us to understand epigenome editing activity. Problematically for point a) and b) it is easier to directly measure gene expression than to measure multiple PTMs and so the real usefulness of this approach mostly relates to c) and d).

Other approaches have been published that use histone PTM to predict expression (e.g. PMID 27587684, 36588793). Is this model better in some way? No comparisons are made although a claim is made that direct comparisons are difficult. I appreciate that the authors have not used the histone PTM data to predict gene expression levels of an "average cell" but rather that they are predicting expression within specific cell types or for unseen cell types. Approaches that predict expression levels are much more useful whereas some previous approaches have only predicted expressed or not expressed or a rank order or bin-based ranking. The paper does not seem to have substantial novel insights into understanding the biology of gene expression.

The approach of using this model to predict epigenetic editor activity on transcription is interesting and to my knowledge novel although only examined in the context of a p300 editor. As the author point out the interpretation of the epigenetic editing data is convoluted by things like sgRNA activity scoring and to fully understand the results likely would require histone PTM profiling and maybe dCas9 ChIP-seq for each sgRNA which would be a substantial amount of work.

Furthermore from the model evaluation of H3K9me3 is seems the model is performing modestly for other forms of epigenetic or transcriptional editing- e.g. we know for the best studied transcriptional editor which is CRISPRi (dCas9-KRAB) that recruitment to a locus is associated with robust gene repression across the genome and is associated with H3K9me3 deposition by recruitment of KAP1/HP1/SETDB1 (PMID: 35688146, 31980609, 27980086, 26501517).

One concern overall with this approach is that dCas9-p300 has been observed to induce sgRNA independent off target H3K27Ac (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8349887/ see Figure S5D) which could convolute interpretation of this type of experiment for the model.

Reviewer #2 (Public review):

Summary:

The authors build a gene expression model based on histone post-translational modifications, and find that H3K27ac is correlated with gene expression. They proceed to perturb H3K27ac at 13 gene promoters in two cell types, and measure gene expression changes to test their model.

Strengths:

The combination of multiple methods to model expression, along with utilizing 6 histone datasets in 13 cell types allowed the authors to build a model that correlates between 0.7-0.79 with gene expression. They use dCas9-p300 fusions to perturb H3K27ac and monitor gene expression to test their model. Ranked correlations of the HEK293 data showed some support for the predictions after perturbation of H3K27ac.

Weaknesses:

The perturbation of 5 genes in K562 with perturb-seq data shows a modest correlation of ~0.5 and isn't included in the main figures. The authors are then left to speculate reasons why the outcome of epigenome editing doesn't fit their predictions, which highlights the limited value in the current version of this method.

As mentioned before, testing genes that were not expressed being most activated by dCas9-p300 weaken the correlations vs. looking at a broad range of different gene expression as the original model was trained on.<br /> If the authors want this method to be used to predict outcomes of epigenome editing, expanding to dCas9-KRAB and other CRISPRa methods (SAM and VPR) would be useful. Those datasets are published and could be analyzed for this manuscript.<br /> The authors don't compare their method to other prediction methods.

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Public Review):

Batra, Cabrera, Spence et al. present a model which integrates histone posttranslational modification (PTM) data across cell models to predict gene expression with the goal of using this model to better understand epigenetic editing. This gene expression prediction model approach is useful if a) it predicts gene expression in specific cell lines b) it predicts expression values rather than a rank or bin, c) it helps us to better understand the biology of gene expression, or d) it helps us to understand epigenome editing activity. Problematically for points a) and b) it is easier to directly measure gene expression than to measure multiple PTMs and so the real usefulness of this approach mostly relates to c) and d).

We thank the reviewer for their comment and we agree that directly measuring gene expression (e.g., by performing RNA-seq) is easier than performing multiple PTMs in a new cell line. We designed our approach keeping in mind that the primary use case is to understand how epigenome editing would affect gene expression.

Other approaches have been published that use histone PTM to predict expression (e.g. 27587684, 36588793). Is this model better in some way? No comparisons are made. The paper does not seem to have substantial novel insights into understanding the biology of gene expression. The approach of using this model to predict epigenetic editor activity on transcription is interesting and to my knowledge novel but I doubt given the variability of the predictions (Figures 6 and S7&8) that many people will be interested in using this in a practical sense. As the authors point out, the interpretation of the epigenetic editing data is convoluted by things like sgRNA activity scoring and to fully understand the results likely would require histone PTM profiling and maybe dCas9 ChIP-seq for each sgRNA which would be a substantial amount of work.

We thank the reviewer for this insightful comment. We have included citations for a series of papers (PMIDs: 27587684, 30147283, 36588793) that performed gene expression prediction using histone PTM data. However, each of these methods perform classification of gene expression as opposed to predicting the actual gene expression value via regression. Additionally, the referenced studies all work with Roadmap Epigenomics read depth data as opposed to p-values obtained from the ENCODE pipelines, making it difficult to make direct comparisons.

We outline in the Discussion section that by creating a comprehensive dataset of epigenome editing outcomes, which include quantification of histone PTMs before and after in situ perturbations, will improve our understanding of the effects of dCas9-p300 on gene expression and assist in the design of gRNAs for achieving fine-tuned control over gene expression levels. 

Furthermore from the model evaluation of H3K9me3 it seems the model is not performing well for epigenetic or transcriptional editing- e.g. we know for the best studied transcriptional editor which is CRISPRi (dCas9-KRAB) that recruitment to a locus is associated with robust gene repression across the genome and is associated with H3K9me3 deposition by recruitment of KAP1/HP1/SETDB1 (PMID: 35688146, 31980609, 27980086, 26501517). However, it seems from Figures 2&4 that the model wouldn't be able to evaluate or predict this.

We thank the reviewer for their comment. We have included a supplementary figure, Figure 4 – figure supplement 1, that quantifies how sensitive the trained gene expression model is to perturbations in H3K9me3. Indeed our data suggests that the model predictions are sensitive to perturbations in H3K9me3. For instance, there is a clear decrease and a gradual increase as the position where the perturbation is performed moves from upstream to downstream of the TSS. Additionally, the magnitude of the predicted fold-change is a function of how much the H3K9me3 is perturbed and hence the magnitude of change would be even higher if the perturbation magnitude is increased. However, this precise magnitude is hard to estimate In the absence of experimental perturbation data for H3K9me3.

The model seems to predict gene expression for endogenous genes quite well although the authors sometimes use expression and sometimes use rank (e.g. Figure 6) - being clearer with how the model predicts expression rather than using rank or fold change would be very useful.

We thank the reviewer for this important suggestion. We have added text in the revised manuscript to clarify that the model predicts gene expression values, which can be interpreted as rank or fold change, depending on the use case.

One concern overall with this approach is that dCas9-p300 has been observed to induce sgRNA-independent off-target H3K27Ac (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8349887/ see Figure S5D) which could convolute interpretation of this type of experiment for the model.

This is an excellent point and indeed, we and others have observed that dCas9-p300 can result in off-target H3K27ac levels (both increased and suppressed) across the genome. However, p300 is one of the few known proteins that can catalyze H3K27ac in the human genome, and H3K27ac remains a proxy for active genomic regulatory elements. Nevertheless, dCas9-p300 off target activity could certainly convolute our approach. We have included language to address this caveat in our discussion. Interestingly, even though dCas9-p300 (and other epigenome editing enzymes) can lead to off-target chromatin modifications, these effects often occur without coincident disruptions to the transcriptome. This suggests that many chromatin modifications, while “supportive” or “instructive” of/for transcription, may be insufficient (either alone or in the context of dCas9-based fusions) for transcriptional effects.

Figure 2

It seems this figure presents known rather than novel findings from the authors' description. Please comment on whether there are any new findings in this figure. Please comment on differences in patterns of repressive and activating histone PTMs between cell lines (e.g. H1-Esc H3K27me3 green 25-50% is more enriched than red 0-25%).

Thank you for pointing out this issue. We have revised the text in both the Results and Discussion sections to better articulate that the goal of this figure is to validate the hypothesis that there are consistent patterns of histone PTMs with respect to gene expression across different human cell types.

In Figure 2, which illustrates the raw histone marks data, the non-monotonic behavior of H3K27me3 in H1-hESC cells is indicative of a real biological phenomenon. This interpretation is supported by the relatively low Pearson correlation for the H3K27me3 mark observed in these cells, as documented in Figure 1b of another study: https://www.biorxiv.org/content/10.1101/2024.03.29.587323v1.

Figure 3&4

There are a number of approaches including DeepChrome and TransferChrome that predict endogenous gene expression from histone PTMs. I appreciate that the authors have not used the histone PTM data to predict gene expression levels of an "average cell" but rather that they are predicting expression within specific cell types or for unseen cell types. But from what is presented it isn't clear that the author's model is better or enabling beyond other approaches. The authors should show their model is better than other approaches or make clear why this is a significant advance that will be enabling for the field. For example is it that in this approach they are actually predicting expression levels whereas previous approaches have only predicted expressed or not expressed or a rank order or bin-based ranking?

We thank the reviewer for this comment. We have added text to clarify the difference between our approach and existing approaches. There are two key differences between our model and other approaches. First, the gene expression model that we have trained here predicts gene expression values instead of gene expression levels as either high or low. Second, we have trained our models on ENCODE p-value data instead of read depths obtained from the Roadmap Epigenomics Consortium.

Figure 5

From the methods, it seems gene activation is measured by qpcr in hek293 transfected with individual sgRNAs and dCas9-p300. The cells aren't selected or sorted before qPCR so how are we sure that some of the variability isn't due to transfection efficiency associated with variable DNA quality or with variable transfection efficiency?

This is a good question. All DNA preps were generated using high-quality reagents and consistent protocols. In addition, the only variable that changed with respect to transfection efficiency was the gRNA-encoding vector used in qPCR assays. We have added new data which demonstrates that transfection efficiency is shared across experiments (Figure 5 – figure supplement 1). We have also added additional experimental data as well as computational analysis analyzing a new dCas9-p300 based Perturb-seq dataset to the manuscript (Figure 6 – figure supplement 1), which use lentiviral transduction and RNA-seq as readouts and thus, are buffered against the variances mentioned by the Reviewer.

Figure 6

The use of rank in 6D and 6E is confusing. In 6D a higher rank is associated with higher expression while in 6E a higher rank seems to mean a lower fold change e.g. CYP17A1 has a low predicted fold-change rank and qPCR fold-change rank but in Figure 5 a very high qPCR fold change. Labeling this more clearly or explaining it in the text further would be useful.

We thank the reviewer for their suggestion. We have made relevant changes to the caption of Figure 6 to clarify this.

Reviewer #2 (Public Review):

Summary:

The authors build a gene expression model based on histone post-translational modifications and find that H3K27ac is correlated with gene expression. They proceed to perturb H3K27ac at 8 gene promoters, and measure gene expression changes to test their model.

Strengths:

The combination of multiple methods to model expression, along with utilizing 6 histone datasets in 13 cell types allowed the authors to build a model that correlates between 0.7-0.79 with gene expression. This group also utilized a tool they are experts in, dCas9-p300 fusions to perturb H3K27ac and monitor gene expression to test their model. Ranked correlations showed some support for the predictions after the perturbation of H3K27ac.

Weaknesses:

The perturbation of only 8 genes, and the only readout being qPCR-based gene expression, as opposed to including H3K27ac, weakened their validation of the computational model. Likewise, the use of six genes that were not expressed being most activated by dCas9-p300 might weaken the correlations vs. looking at a broad range of different gene expressions as the original model was trained on.

We thank the reviewer for their comments. We have added additional experimental data as well as computational analysis analyzing a new dCas9-p300 based Perturb-seq dataset to the manuscript. We observe that the models we have developed are able to predict the fold-change rank across genes reasonably well (Figure 6 – figure supplement 1), similar to what we observe in Figure 6E.

Reviewer #1 (Recommendations For The Authors):

The authors should comment on how their model is different from or better than other models that use histone PTM data to predict gene expression.

We thank the reviewer for this insightful suggestion. We have included citations for a series of papers (PMIDs: 27587684, 30147283, 36588793) that performed gene expression prediction using histone PTM data. However, each of these methods perform classification of gene expression as opposed to predicting the actual gene expression value via regression. Additionally, the referenced studies all work with Roadmap Epigenomics read depth data as opposed to p-values obtained from the ENCODE pipelines, making it difficult to make direct comparisons.

The authors need to make clear whether their model will apply to other common epigenetic or transcriptional editors such as CRISPRi/H3K9me3 which is widely used.

In this study, we focus on the histone changes induced by p300. However, future studies may use the framework described in our manuscript and apply it to other transcriptional editors as well.

The authors need to be clearer about where they are predicting expression and where they are using rank. Ideally, show both.

We thank the reviewer for this important suggestion. We have added text in the revised manuscript to clarify that the model predicts gene expression values, which can be interpreted as rank or fold change, depending on the use case.

The authors should ideally show a case where they use the model to make a prediction of genes that can and can not be activated by dCas9-p300 or other epigenetic editors and then prove this with experiments.

Thank you for the excellent suggestion. While it is indeed relevant, exploring this would extend beyond the scope of our current study. We consider it a valuable topic for future research.

Reviewer #2 (Recommendations For The Authors):

The y-axis in 5C needs to be labeled. The authors state it is "relative mRNA" but these numbers correlated with fold changes shown in Table S2.

We have clarified the definition of the Y-axis in the caption for Figure 5C.

eLife Assessment

This is an important study showing that people who are hungry (vs. sated) put more weight on taste (vs. health) in their food choices. The experiment is well-designed and includes choice behavior, eye-tracking, and state-of-the-art computational modeling, resulting in compelling evidence supporting the conclusions. The manuscript could be further improved through appropriate revisions to data analysis and interpretation.

Reviewer #1 (Public review):

Summary:

In this article, the authors set out to understand how people's food decisions change when they are hungry vs. sated. To do so, they used an eye-tracking experiment where participants chose between two food options, each presented as a picture of the food plus its "Nutri-Score". In both conditions, participants fasted overnight, but in the sated condition, participants received a protein shake before making their decisions. The authors find that participants in the hungry condition were more likely to choose the tastier option. Using variants of the attentional drift-diffusion model, they further find that the best-fitting model has different attentional discounts on the taste and health attributes and that the attentional discount on the health information was larger for the hungry participants.

Strengths:

The article has many strengths. It uses a food-choice paradigm that is established in neuroeconomics. The experiment uses real foods, with accurate nutrition information, and incentivized choices. The experimental manipulation is elegant in its simplicity - administering a high-calorie protein shake. It is also commendable that the study was within-participant. The experiment also includes hunger and mood ratings to confirm the effectiveness of the manipulation. The modeling work is impressive in its rigor - the authors test 9 different variants of the DDM, including recent models like the mtDDM and maaDDM, as well as some completely new variants (maaDDM2phi and 2phisp). The model fits decisively favor the maaDDM2phi.

Weaknesses:

First, in examining some of the model fits in the supplements, e.g. Figures S9, S10, S12, S13, it looks like the "taste weight" parameter is being constrained below 1. Theoretically, I understand why the authors imposed this constraint, but it might be unfairly penalizing these models. In theory, the taste weight could go above 1 if participants had a negative weight on health. This might occur if there is a negative correlation between attractiveness and health and the taste ratings do not completely account for attractiveness. I would recommend eliminating this constraint on the taste weight.

Second, I'm not sure about the mediation model. Why should hunger change the dwell time on the chosen item? Shouldn't this model instead focus on the dwell time on the tasty option?

Third, while I do appreciate the within-participant design, it does raise a small concern about potential demand effects. I think the authors' results would be more compelling if they replicated when only analyzing the first session from each participant. Along similar lines, it would be useful to know whether there was any effect of order.

Fourth, the authors report that tasty choices are faster. Is this a systematic effect, or simply due to the fact that tasty options were generally more attractive? To put this in the context of the DDM, was there a constant in the drift rate, and did this constant favor the tasty option?

Fifth, I wonder about the mtDDM. What are the units on the "starting time" parameters? Seconds? These seem like minuscule effects. Do they align with the eye-tracking data? In other words, which attributes did participants look at first? Was there a correlation between the first fixations and the relative starting times? If not, does that cast doubt on the mtDDM fits? Did the authors do any parameter recovery exercises on the mtDDM?

Reviewer #2 (Public review):

Summary:

This study investigates the effect of a fed vs hungry state on food decision-making.

70 participants performed a computerized food choice task with eye tracking. Food images came from a validated set with variability in food attributes. Foods ranged from low caloric density unprocessed (fruits) to high caloric density processed foods (chips and cookies).

Prior to the choice task participants rated images for taste, health, wanting, and calories. In the choice task participants simply selected one of two foods. They were told to pick the one they preferred. Screens consisted of two food pictures along with their "Nutri-Score". They were told that one preferred food would be available for consumption at the end.

A drift-diffusion model (DDM) was fit to the reaction time values. Eye tracking was used to measure dwell time on each part of the monitor.

Findings:

Participants tended to select the item they had rated as "tastier", however, health also contributed to decisions.

Strengths:

The most interesting and innovative aspect of the paper is the use of the DDM models to infer from reaction time and choice the relative weight of the attributes.

Were the ratings redone at each session? E.g. were all tastiness ratings for the sated session made while sated? This is relevant as one would expect the ratings of tastiness and wanting to be affected by the current fed state.

Weaknesses:

My main criticism, which doesn't affect the underlying results, is that the labeling of food choices as being taste- or health-driven is misleading. Participants were not cued to select health vs taste. Studies in which people were cued to select for taste vs health exist (and are cited here). Also, the label "healthy" is misleading, as here it seems to be strongly related to caloric density. A high-calorie food is not intrinsically unhealthy (even if people rate it as such). The suggestion that hunger impairs making healthy decisions is not quite the correct interpretation of the results here (even though everyone knows it to be true). Another interpretation is that hungry people in negative calorie balance simply prefer more calories.

Reviewer #3 (Public review):

Summary:

This well-powered study tested the effects of hunger on value-based dietary decision-making. The main hypothesis was that attentional mechanisms guide choices toward unhealthier and tastier options when participants are hungry and are in the fasted state compared to satiated states. Participants were tested twice - in a fasted state and in a satiated state after consuming a protein shake. Attentional mechanisms were measured during dietary decision-making by linking food choices and reaction times to eye-tracking data and mathematical drift-diffusion models. The results showed that hunger makes high-conflict food choices more taste-driven and less health-driven. This effect was formally mediated by relative dwell time, which approximates attention drawn to chosen relative to unchosen options. Computational modeling showed that a drift-diffusion model, which assumed that food choices result from a noisy accumulation of evidence from multiple attributes (i.e., taste and health) and discounted non-looked attributes and options, best explained observed choices and reaction times.

Strengths:

This study's findings are valuable for understanding how energy states affect decision-making and provide an answer to how hunger can lead to unhealthy choices. These insights are relevant to psychology, behavioral economics, and behavioral change intervention designs.

The study has a well-powered sample size and hypotheses were pre-registered. The analyses comprised classical linear models and non-linear computational modeling to offer insight into putative cognitive mechanisms.

In summary, the study advances the understanding of the links between energy states and value-based decision-making by showing that depleting is powerful for shaping the formation of food preferences. Moreover, the computational analysis part offers a plausible mechanistic explanation at the algorithmic level of observed effects.

Weaknesses:

Some parts of the positioning of the hunger state manipulation and the interpretation of its effects could be improved.

On the positioning side, it does not seem like a 'bad' decision to replenish energy states when hungry by preferring tastier, more often caloric options. In this sense, it is unclear whether the observed behavior in the fasted state is a fallacy or a response to signals from the body. The introduction does mention these two aspects of preferring more caloric food when hungry. However, some ambiguity remains about whether the study results indeed reflect suboptimal choice behavior or a healthy adaptive behavior to restore energy stores.

On the interpretation side, previous work has shown that beliefs about the nourishing and hunger-killing effectiveness of drinks or substances influence subjective and objective markers of hunger, including value-based dietary decision-making, and attentional mechanisms approximated by computational models and the activation of cognitive control regions in the brain. The present study shows differences between the protein shake and a natural history condition (fasted, state). This experimental design, however, cannot rule between alternative interpretations of observed effects. Notably, effects could be due to (a) the drink's active, nourishing ingredients, (b) consuming a drink versus nothing, or (c) both.

Author response:

Reviewer 1:

(1) We appreciate the reviewer’s suggestion to test a multi-attribute attentional drift-diffusion model (maaDDM) that does not constrain the taste and health weights to the range of 0 and 1 and will test such a model.

(2) Similarly, we will follow the reviewer’s suggestion to address potential demand effects. First, we will add “order” (binary: hungry-sated or sated hungry) as a predictor to our GLMM, to test for potential systematic effects of order on choices and response times. Second, we will split the participants by “order” and examine whether we see group differences of tasty and healthy decisions within the first testing session. Note that we already anticipate that looking at only 50% of the data and testing for a between-subject rather than within-subject effect is likely to reduce effect size and statistical sensitivity.

(3) We thank the reviewer for their observant remark about faster tasty choices and potential markers in the drift rate. While our starting point models show that there might be a small starting point bias towards the taste boundary which result in faster decisions, we will take a closer look at the simulated value differences as obtained in our posterior predictive checks to see if the drift rate is systematically more extreme for tasty choices.

(4) Regarding the mtDDM, we will verify that the relative starting time (rst) effects are minuscule. While we will follow the recommendation of correlating first fixations with rst, we would like to point out that a majority of fixations (see Figure 3b) and first fixations (see Figure S6b) are on food images. We will also provide a parameter recovery of the mtDDM.

Reviewer 2:

(1) We would like to verify the reviewer’s interpretation that hungry people in negative calorie balance simply prefer more calories and would like to point to our supplementary analyses, in which we show that hunger state also increases the probability of higher wanted and higher caloric decisions (see SOM4, SOM5, Figure S4). Moreover, we agree that high caloric items might not be unhealthy and are happy to demonstrate the correlations between health ratings and objective caloric content, to demonstrate the strong negative correlation in our dataset, which our principal component analyses hints at, too.

Reviewer 3:

(1) We agree that choosing tasty over healthy options under hunger may be evolutionarily adaptive. We will address the adaptiveness of this hunger driven mechanism in our discussion, reiterating the differentiation made in the introduction that this system no longer be adaptive in our obesogenic environment, leading to suboptimal decisions.

(2) We will address alternative explanations of the observed effects in our discussion with respect to the macro-nutritional content of the Shake and potential placebo effects arising from the shake vs no shake manipulation.

eLife Assessment

This valuable study presents a meta-analysis of the literature, confirming the relationship between the coupling of slow oscillations and fast spindles in memory formation, although the reported effects are weak and should be more clearly justified. Furthermore, while the evidence is convincing overall, the manuscript provides an incomplete description of the methods, which may challenge comprehension for readers unfamiliar with advanced statistical techniques. This study will be of interest to neuroscientists focusing on sleep and memory.

Reviewer #1 (Public review):

In this meta-analysis, Ng and colleagues review the association between slow-oscillation spindle coupling during sleep and overnight memory consolidation. The coupling of these oscillations (and also hippocampal sharp-wave ripples) have been central to theories and mechanistic models of active systems consolidation, that posit that the coupling between ripples, spindles, and slow oscillations (SOs) coordinate and drive the coordinated reactivation of memories in hippocampus and cortex, facilitating cross-regional information and ultimately memory strengthening and stabilisation.

Given the importance that these coupling mechanisms have been given in theory, this is a timely and important contribution to the literature in terms of determining whether these theoretical assumptions hold true in human data. The results show that the timing of sleep spindles relative to the SO phase, and the consistency of that timing, predicted overnight memory consolidation in meta-analytic models. The overall amount of coupling events did not show as strong a relationship. The coupling phase in particular was moderated by a number of variables including spindle type (fast, slow), channel location (frontal, central, posterior), age, and memory type. The main takeaway is that fast spindles that consistently couple close to the peak of the SO in frontal channel locations are optimal for memory consolidation, in line with theoretical predictions.

I did not follow the logic behind including spindle amplitude in the meta-analysis. This is not a measure of SO-spindle coupling (which is the focus of the review), unless the authors were restricting their analysis of the amplitude of coupled spindles only. It doesn't sound like this is the case though. The effect of spindle amplitude on memory consolidation has been reviewed in another recent meta-analysis (Kumral et al, 2023, Neuropsychologia). As this isn't a measure of coupling, it wasn't clear why this measure was included in the present meta-analysis. You could easily make the argument that other spindle measures (e.g., density, oscillatory frequency) could also have been included, but that seems to take away from the overall goal of the paper which was to assess coupling.

At the end of the first paragraph of section 3.1 (page 13), the authors suggest their results "... further emphasise the role of coupling compared to isolated oscillation events in memory consolidation". This had me wondering how many studies actually test this. For example, in a hierarchical regression model, would coupled spindles explain significantly more variance than uncoupled spindles? We already know that spindle activity, independent of whether they are coupled or not, predicts memory consolidation (e.g., Kumral meta-analysis). Is the variance in overnight memory consolidation fully explained by just the coupled events? If both overall spindle density and coupling measures show an equal association with consolidation, then we couldn't conclude that coupling compared to isolated events is more important.

It was very interesting to see that the relationship between the fast spindle coupling phase and overnight consolidation was strongest in the frontal electrodes. Given this, I wonder why memory promoting fast spindles shows a centro-parietal topography? Surely it would be more adaptive for fast spindles to be maximally expressed in frontal sites. Would a participant who shows a more frontal topography of fast spindles have better overnight consolidation than someone with a more canonical centro-parietal topography? Similarly, slow spindles would then be perfectly suited for memory consolidation given their frontal distribution, yet they seem less important for memory.

The authors rightly note the issues with multiple comparisons in sleep physiology and memory studies. Multiple comparison issues arise in two ways in this literature. First are comparisons across multiple electrodes (many studies now use high-density systems with 64+ channels). Second are multiple comparisons across different outcome variables (at least 3 ways to quantify coupling (phase, consistency, occurrence) x 2 spindle types (fast, slow). Can the authors make some recommendations here in terms of how to move the field forward, as this issue has been raised numerous times before (e.g., Mantua 2018, Sleep; Cox & Fell 2020, Sleep Medicine Reviews for just a couple of examples). Should researchers just be focusing on the coupling phase? Or should researchers always report all three metrics of coupling, and correct for multiple comparisons? I think the use of pre-registration would be beneficial here, and perhaps could be noted by the authors in the final paragraph of section 3.5, where they discuss open research practices.

Reviewer #2 (Public review):

Summary:

This article reviews the studies on the relationship between slow oscillation (SO)-spindle (SP) coupling and memory consolidation. It innovatively employs non-normal circular linear correlations through a Bayesian meta-analysis. A systematic analysis of the retrieved studies highlighted that co-coupling of SO and the fast SP's phase and amplitude at the frontal part better predicts memory consolidation performance. I only have a few comments that I recommend are addressed.

Major Comments:

Regarding the Moderator of Age: Although the authors discuss the limited studies on the analysis of children and elders regarding age as a moderator, the figure shows a significant gap between the ages of 40 and 60. Furthermore, there are only a few studies involving participants over the age of 60. Given the wide distribution of effect sizes from studies with participants younger than 40, did the authors test whether removing studies involving participants over 60 would still reveal a moderator effect?

Reviewer #3 (Public review):

This manuscript presents a meta-analysis of 23 studies, which report 297 effect sizes, on the effect of SO-spindle coupling on memory performance. The analysis has been done with great care, and the results are described in great detail. In particular, there are separate analyses for coupling phase, spindle amplitude, coupling strength (e.g., measured by vector length or modulation index), and coupling percentage (i.e., the percentage of SPs coupled with SOs). The authors conclude that the precision and strength of coupling showed significant correlations with memory retention.

There are two main points where I do not agree with the authors.

First, the authors conclude that "SO-SP coupling should be considered as a general physiological mechanism for memory consolidation". However, the reported effect sizes are smaller than what is typically considered a "small effect" (0.10<br /> Second, the study implements state-of-the-art Bayesian statistics. While some might see this as a strength, I would argue that it is the greatest weakness of the manuscript. A classical meta-analysis is relatively easy to understand, even for readers with only a limited background in statistics. A Bayesian analysis, on the other hand, introduces a number of subjective choices that render it much less transparent. This becomes obvious in the forest plots. It is not immediately apparent to the reader how the distributions for each study represent the reported effect sizes (gray dots). Presumably, they depend on the Bayesian priors used for the analysis. The use of these priors makes the analyses unnecessarily opaque, eventually leading the reader to question how much of the findings depend on subjective analysis choices (which might be answered by an additional analysis in the supplementary information). However, most of the methods are not described in sufficient detail for the reader to understand the proceedings. It might be evident for an expert in Bayesian statistics what a "prior sensitivity test" and a "posterior predictive check" are, but I suppose most readers would wish for a more detailed description. However, using a "Markov chain Monte Carlo (MCMC) method with the no-U-turn Hamiltonian Monte Carlo (HMC) sampler" and checking its convergence "through graphical posterior predictive checks, trace plots, and the Gelman and Rubin Diagnostic", which should then result in something resembling "a uniformly undulating wave with high overlap between chains" is surely something only rocket scientists understand. Whether this was done correctly in the present study cannot be ascertained because it is only mentioned in the methods and no corresponding results are provided. This kind of analysis seems not to be made to be intelligible to the average reader. It follows a recent trend of using more and more opaque methods. Where we had to trust published results a decade ago because the data were not openly available, today we must trust the results because the methods can no longer be understood with reasonable effort.

In one point the method might not be sufficiently justified. The method used to transform circular-linear r (actually, all references cited by the authors for circular statistics use r² because there can be no negative values) into "Z_r", seems partially plausible and might be correct under the H0. However, Figure 12.3 seems to show that under the alternative Hypothesis H1, the assumptions are not accurate (peak Z_r=~0.70 for r=0.65). I am therefore, based on the presented evidence, unsure whether this transformation is valid. Also, saying that Z_r=-1 represents the null hypothesis and Z_r=1 the alternative hypothesis can be misinterpreted, since Z_r=0 also represents the null hypothesis and is not half way between H0 and H1.

Author response:

Reviewer #1 (Public review):

I did not follow the logic behind including spindle amplitude in the meta-analysis. This is not a measure of SO-spindle coupling (which is the focus of the review), unless the authors were restricting their analysis of the amplitude of coupled spindles only. It doesn't sound like this is the case though. The effect of spindle amplitude on memory consolidation has been reviewed in another recent meta-analysis (Kumral et al, 2023, Neuropsychologia). As standardization this isn't a measure of coupling, it wasn't clear why this measure was included in the present meta-analysis. You could easily make the argument that other spindle measures (e.g., density, oscillatory frequency) could also have been included, but that seems to take away from the overall goal of the paper which was to assess coupling.

Indeed, spindle amplitude refers to all spindle events rather than only coupled spindles. This choice was made because we recognized the challenge of obtaining relevant data from each study—only 4 out of the 23 included studies performed their analyses after separating coupled and uncoupled spindles. This inconsistency strengthens the urgency and importance of this meta-analysis to standardize the methods and measures used for future analysis on SO-SP coupling and beyond. We agree that focusing on the amplitude of coupled spindles would better reveal their relations with coupling, and we will discuss this limitation in the manuscript.

Nevertheless, we believe including spindle amplitude in our study remains valuable, as it served several purposes. First, SO-SP coupling involves the modulation between spindle amplitude and slow oscillation phase. Different studies have reported conflicting conclusions regarding how spindle amplitude was related to coupling– some found significant correlations (e.g., Baena et al., 2023), while others did not (e.g., Roebber et al., 2022). This discrepancy highlights an indirect but potentially crucial insight into the role of spindle amplitude in coupling dynamics. Second, in studies related to SO-SP coupling, spindle amplitude is one of the most frequently reported measures along with other coupling measures that significantly correlated with oversleep memory improvements (e.g. Kurz et al., 2023; Ladenbauer et al., 2021; Niknazar et al., 2015), so we believe that including this measure can more comprehensively review of the existing literature on SO-SP coupling. Third, incorporating spindle amplitude allows for a direct comparison between the measurement of coupling and individual events alone in their contribution to memory consolidation– a question that has been extensively explored in recent research. (e.g., Hahn et al., 2020; Helfrich et al., 2019; Niethard et al., 2018; Weiner et al., 2023). Finally, spindle amplitude was identified as a key moderator for memory consolidation in Kumral et al.'s (2023) meta-analysis. By including it in our analysis, we sought to replicate their findings within a broader framework and introduce conceptual overlaps with existing reviews. Therefore, although we were not able to selectively include coupled spindles, there is still a unique relation between spindle amplitude and SO-SP coupling that other spindle measures do not have. 

Originally, we also intended to include coupling density or counts in the analysis, which seems more relevant to the coupling metrics. However, the lack of uniformity in methods used to measure coupling density posed a significant limitation. We hope that our study will encourage consistent reporting of all relevant parameters in future research, enabling future meta-analyses to incorporate these measures comprehensively. We will add this discussion to the manuscript in the revised version to further clarify these points.

References:

Roebber, J. K., Lewis, P. A., Crunelli, V., Navarrete, M. & Hamandi, K. Effects of anti-seizure medication on sleep spindles and slow waves in drug-resistant epilepsy. Brain Sci. 12, 1288 (2022). https://doi.org/10.3390/brainsci12101288

All other citations were referenced in the manuscript.

At the end of the first paragraph of section 3.1 (page 13), the authors suggest their results "... further emphasise the role of coupling compared to isolated oscillation events in memory consolidation". This had me wondering how many studies actually test this. For example, in a hierarchical regression model, would coupled spindles explain significantly more variance than uncoupled spindles? We already know that spindle activity, independent of whether they are coupled or not, predicts memory consolidation (e.g., Kumral meta-analysis). Is the variance in overnight memory consolidation fully explained by just the coupled events? If both overall spindle density and coupling measures show an equal association with consolidation, then we couldn't conclude that coupling compared to isolated events is more important.

While primary coupling measurements, including coupling phase and strength, showed strong evidence for their associations with memory consolidation, measures of spindles, including spindle amplitude, only exhibited limited evidence (or “non-significant” effect) for their association with consolidation. These results are consistent with multiple empirical studies using different techniques (e.g., Hahn et al., 2020; Helfrich et al., 2019; Niethard et al., 2018; Weiner et al., 2023), which reported that coupling metrics are more robust predictors of consolidation and synaptic plasticity than spindle or slow oscillation metrics alone. However, we agree with the reviewer that we did not directly separate the effect between coupled and uncoupled spindles, and a more precise comparison would involve contrasting the “coupling of oscillation events” with ”individual oscillation events” rather than coupling versus isolated events.

We recognized that Kumral and colleagues’ meta-analysis reported a moderate association between spindle measures and memory consolidation (e.g., for spindle amplitude-memory association they reported an effect size of approximately r = 0.30). However, one of the advantages of our study is that we actively cooperated with the authors to obtain a large number of unreported and insignificant data relevant to our analysis, as well as separated data that were originally reported under mixed conditions. This approach decreases the risk of false positives and selective reporting of results, making the effect size more likely to approach the true value. In contrast, we found only a weak effect size of r = 0.07 with minimal evidence for spindle amplitude-memory relation. However, we agree with the reviewer that using a more conservative term in this context would be a better choice since we did not measure all relevant spindle metrics including the density.

To improve clarity in our manuscript, we will revise the statement to: “Together with other studies included in the review, our results suggest a crucial role of coupling but did not support the role of spindle events alone in memory consolidation,” and provide relevant references. We believe this can more accurately reflect our findings and the existing literature to address the reviewer’s concern.

It was very interesting to see that the relationship between the fast spindle coupling phase and overnight consolidation was strongest in the frontal electrodes. Given this, I wonder why memory promoting fast spindles shows a centro-parietal topography? Surely it would be more adaptive for fast spindles to be maximally expressed in frontal sites. Would a participant who shows a more frontal topography of fast spindles have better overnight consolidation than someone with a more canonical centro-parietal topography? Similarly, slow spindles would then be perfectly suited for memory consolidation given their frontal distribution, yet they seem less important for memory.

Regarding the topography of fast spindles and their relationship to memory consolidation, we agree this is an intriguing issue, and we have already developed significant progress in this topic in our ongoing work. We share a few relevant observations: First, there are significant discrepancies in the definition of “slow spindle” in the field. Some studies defined slow spindle from 9-12 Hz (e.g. Mölle et al., 2011; Kurz et al., 2021), while others performed the event detection within a range of 11-13/14 Hz (e.g. Barakat et al., 2011; D'Atri et al., 2018). Compounding this issue, individual differences in spindle frequency are often overlooked, leading to challenges in reliably distinguishing between slow and fast spindles. Some studies have reported difficulty in clearly separating the two types of spindles altogether (e.g., Hahn et al., 2020). Moreover, a critical factor often ignored in past research is the traveling nature of both slow oscillations and spindles across the cortex, where spindles are coupled with significantly different phases of slow oscillations (see Figure 5). We believe a better understanding of coupling in the context of the movement of these waves will help us better understand the observed frontal relationship with consolidation. We will address this in our revised manuscript.

The authors rightly note the issues with multiple comparisons in sleep physiology and memory studies. Multiple comparison issues arise in two ways in this literature. First are comparisons across multiple electrodes (many studies now use high-density systems with 64+ channels). Second are multiple comparisons across different outcome variables (at least 3 ways to quantify coupling (phase, consistency, occurrence) x 2 spindle types (fast, slow). Can the authors make some recommendations here in terms of how to move the field forward, as this issue has been raised numerous times before (e.g., Mantua 2018, Sleep; Cox & Fell 2020, Sleep Medicine Reviews for just a couple of examples). Should researchers just be focusing on the coupling phase? Or should researchers always report all three metrics of coupling, and correct for multiple comparisons? I think the use of pre-registration would be beneficial here, and perhaps could be noted by the authors in the final paragraph of section 3.5, where they discuss open research practices.

There are indeed multiple methods that we can discuss, including cluster-based and non-parametric methods, etc., to correct for multiple comparisons in EEG data with spatiotemporal structures. In addition, encouraging the reporting of all tested but insignificant results, at least in supplementary materials, is an important practice that helps readers understand the findings with reduced bias. We agree with the reviewer’s suggestions and will add more information in section 3.5 to advocate for a standardized “template” used to analyze and report effect size in future research.

We advocate for the standardization of reporting all three coupling metrics– phase, consistency, and occurrence. Each coupling metric captures distinct properties of the coupling process and may interact with one another (Weiner et al., 2023). Therefore, we believe it is essential to report all three metrics to comprehensively explore their different roles in the “how, what, and where” of long-distance communication and consolidation of memory. As we advance toward a deeper understanding of the relationship between memory and sleep, we hope this work establishes a standard for the standardization, transparency, and replication of relevant studies.

Reviewer #2 (Public review):

Regarding the Moderator of Age: Although the authors discuss the limited studies on the analysis of children and elders regarding age as a moderator, the figure shows a significant gap between the ages of 40 and 60. Furthermore, there are only a few studies involving participants over the age of 60. Given the wide distribution of effect sizes from studies with participants younger than 40, did the authors test whether removing studies involving participants over 60 would still reveal a moderator effect?

We agree that there is an age gap between younger and older adults, as current studies often focus on contrasting newly matured and fully aged populations to amplify the effect, while neglecting the gradual changes in memory consolidation mechanisms across the aging spectrum. We suggest that a non-linear analysis of age effects would be highly valuable, particularly when additional child and older adult data become available.

In response to the reviewer’s suggestion, we re-tested the moderation effect of age after excluding effect sizes from older adults. The results revealed a decrease in the strength of evidence for phase-memory association due to increased variability, but were consistent for all other coupling parameters. The mean estimations also remained consistent (coupling phase-memory relation: -0.005 [-0.013, 0.004], BF10 = 5.51, the strength of evidence reduced from strong to moderate; coupling strength-memory relation: -0.005 [-0.015, 0.008], BF10 = 4.05, the strength of evidence remained moderate). These findings align with prior research, which typically observed a weak coupling-memory relationship in older adults during aging (Ladenbauer et al, 2021; Weiner et al., 2023) but not during development (Hahn et al., 2020; Kurz et al., 2021; Kurz et al., 2023). Therefore, this result is not surprising to us, and there are still observable moderate patterns in the data. We will report these additional results in the revised manuscript, and interpret “the moderator effect of age becomes less pronounced during development after excluding the older adult data”. We believe the original findings including the older adult group remain meaningful after cautious interpretation, given that the older adult data were derived from multiple studies and different groups.

Reviewer #3 (Public review):

First, the authors conclude that "SO-SP coupling should be considered as a general physiological mechanism for memory consolidation". However, the reported effect sizes are smaller than what is typically considered a "small effect" (0.10)

While we acknowledge the concern about the small effect sizes reported in our study, it is important to contextualize these findings within the field of neuroscience, particularly memory research. Even in individual studies, small effect sizes are not uncommon due to the inherent complexity of the mechanisms involved and the multitude of confounding variables. This is an important factor to be considered in meta-analyses where we synthesize data from diverse populations and experimental conditions. For example, the relationship between SO-slow SP coupling and memory consolidation in older adults is expected to be insignificant.

As Funder and Ozer (2019) concluded in their highly cited paper, an effect size of r = 0.3 in psychological and related fields should be considered large, with r = 0.4 or greater likely representing an overestimation and rarely found in a large sample or in a replication. Therefore, we believe r = 0.1 should not be considered as a lower bound of the small effect. Bakker et al. (2019) also advocate for a contextual interpretation of the effect size. This is particularly important in meta-analyses, where the results are less prone to overestimation compared to individual studies, and we cooperated with all authors to include a large number of unreported and insignificant results. In this context, small correlations may contain substantial meaningful information to interpret. Although we agree that effect sizes reported in our study are indeed small at the overall level, they reflect a rigorous analysis that incorporates robust evidence across different levels of moderators. Our moderator analyses underscore the dynamic nature of coupling-memory relationships, with certain subgroups demonstrating much stronger and more meaningful effects, especially after excluding slow spindles and older adults. For example, both the coupling phase and strength of frontal fast spindles with slow oscillations exhibited "moderate-to-large" correlations with the consolidation of different types of memory, especially in young adults, with r values ranging from 0.18 to 0.32. (see Table S9.1-9.4). We will add more discussion about the influence of moderators on the dynamics of coupling-memory associations. In addition, we will update the conclusion to be “SO-fast SP coupling should be considered as a general physiological mechanism for memory consolidation”.

Reference:

Funder, D. C. & Ozer, D. J. Evaluating effect size in psychological research: sense and nonsense. Adv. Methods Pract. Psychol. Sci. 2, 156–168 (2019). https://doi.org/10.1177/2515245919847202.

Bakker, A. et al. Beyond small, medium, or large: Points of consideration when interpreting effect sizes. Educ. Stud. Math. 102, 1–8 (2019). https://doi.org/10.1007/s10649-019-09908-4

Second, the study implements state-of-the-art Bayesian statistics. While some might see this as a strength, I would argue that it is the greatest weakness of the manuscript. A classical meta-analysis is relatively easy to understand, even for readers with only a limited background in statistics. A Bayesian analysis, on the other hand, introduces a number of subjective choices that render it much less transparent.

This kind of analysis seems not to be made to be intelligible to the average reader. It follows a recent trend of using more and more opaque methods. Where we had to trust published results a decade ago because the data were not openly available, today we must trust the results because the methods can no longer be understood with reasonable effort.

This becomes obvious in the forest plots. It is not immediately apparent to the reader how the distributions for each study represent the reported effect sizes (gray dots). Presumably, they depend on the Bayesian priors used for the analysis. The use of these priors makes the analyses unnecessarily opaque, eventually leading the reader to question how much of the findings depend on subjective analysis choices (which might be answered by an additional analysis in the supplementary information).

We appreciate the reviewer for sharing this viewpoint and we value the opportunity to clarify some key points. To address the concern about clarity, we will include a sub-section in the methods section explaining how to interpret Bayesian statistics including priors, posteriors, and Bayes factors, making our results more accessible to those less familiar with this approach.

On the use of Bayesian models, we believe there may have been a misunderstanding. Bayesian methods, far from being "opaque" or overly complex, are increasingly valued for their ability to provide nuanced, accurate, and transparent inferences (Sutton & Abrams, 2001; Hackenberger, 2020; van de Schoot et al., 2021; Smith et al., 1995; Kruschke & Liddell, 2018). It has been applied in more than 1,200 meta-analyses as of 2020 (Hackenberger, 2020). In our study, we used priors that assume no effect (mean set to 0, which aligns with the null) while allowing for a wide range of variation to account for large uncertainties. This approach reduces the risk of overestimation or false positives and demonstrates much-improved performance over traditional methods in handling variability (Williams et al., 2018; Kruschke & Liddell, 2018). Sensitivity analyses reported in the supplemental material (Table S9.1-9.4) confirmed the robustness of our choices of priors– our results did not vary by setting different priors.

As Kruschke and Liddell (2018) described, “shrinkage (pulling extreme estimates closer to group averages) helps prevent false alarms caused by random conspiracies of rogue outlying data,” a well-known advantage of Bayesian over traditional approaches. This explains the observed differences between the distributions and grey dots in the forest plots. Unlike p-values, which can be overestimated with a large sample size and underestimated with a small sample size, Bayesian methods make assumptions explicit, enabling others to challenge or refine them– an approach aligned with open science principles (van de Schoot et al., 2021). For example, a credible interval in Bayesian model can be interpreted as “there is a 95% probability that the parameter lies within the interval.”, while a confidence interval in frequentist model means “In repeated experiments, 95% of the confidence intervals will contain the true value.” We believe the former is much more straightforward and convincing for readers to interpret. We will ensure our justification for using Bayesian models is more clearly presented in the manuscript.

We acknowledge that even with these justifications, different researchers may still have discrepancies in their preferences for Bayesian and frequentist models. To increase the effort of transparent reporting, we have also reported the traditional frequentist meta-analysis results in Supplemental Material 10 to justify the robustness of our analysis, which suggested non-significant differences between Bayesian and frequentist models. We will include clearer references in the next version of the manuscript to direct readers to the figures that report the statistics provided by traditional models.

References:

Hackenberger, B.K. Bayesian meta-analysis now—let's do it. Croat. Med. J. 61, 564–568 (2020). https://doi.org/10.3325/cmj.2020.61.564

Sutton, A.J. & Abrams, K.R. Bayesian methods in meta-analysis and evidence synthesis. Stat. Methods Med. Res. 10, 277–303 (2001). https://doi.org/10.1177/096228020101000404

Williams, D.R., Rast, P. & Bürkner, P.C. Bayesian meta-analysis with weakly informative prior distributions. PsyArXiv (2018). https://doi.org/10.31234/osf.io/9n4zp

van de Schoot, R., Depaoli, S., King, R. et al. Bayesian statistics and modelling. Nat Rev Methods Primers 1, 1 (2021). https://doi.org/10.1038/s43586-020-00001-2

Smith, T.C., Spiegelhalter, D.J. & Thomas, A. Bayesian approaches to random-effects meta-analysis: a comparative study. Stat. Med. 14, 2685–2699 (1995). https://doi.org/10.1002/sim.4780142408

Kruschke, J.K. & Liddell, T.M. The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective. Psychon. Bull. Rev. 25, 178–206 (2018). https://doi.org/10.3758/s13423-016-1221-4

However, most of the methods are not described in sufficient detail for the reader to understand the proceedings. It might be evident for an expert in Bayesian statistics what a "prior sensitivity test" and a "posterior predictive check" are, but I suppose most readers would wish for a more detailed description. However, using a "Markov chain Monte Carlo (MCMC) method with the no-U-turn Hamiltonian Monte Carlo (HMC) sampler" and checking its convergence "through graphical posterior predictive checks, trace plots, and the Gelman and Rubin Diagnostic", which should then result in something resembling "a uniformly undulating wave with high overlap between chains" is surely something only rocket scientists understand. Whether this was done correctly in the present study cannot be ascertained because it is only mentioned in the methods and no corresponding results are provided. 

We appreciate the reviewer’s concerns about accessibility and potential complexity in our descriptions of Bayesian methods. Our decision to provide a detailed account serves to enhance transparency and guide readers interested in replicating our study. We acknowledge that some terms may initially seem overwhelming. These steps, such as checking the MCMC chain convergence and robustness checks, are standard practices in Bayesian research and are analogous to “linearity”, “normality” and “equal variance” checks in frequentist analysis. We have provided exemplary plots in the supplemental material and will add more details to explain the interpretation of these convergence checks. We hope this will help address any concerns about methodological rigor.

In one point the method might not be sufficiently justified. The method used to transform circular-linear r (actually, all references cited by the authors for circular statistics use r² because there can be no negative values) into "Z_r", seems partially plausible and might be correct under the H0. However, Figure 12.3 seems to show that under the alternative Hypothesis H1, the assumptions are not accurate (peak Z_r=~0.70 for r=0.65). I am therefore, based on the presented evidence, unsure whether this transformation is valid. Also, saying that Z_r=-1 represents the null hypothesis and Z_r=1 the alternative hypothesis can be misinterpreted, since Z_r=0 also represents the null hypothesis and is not half way between H0 and H1.

First, we realized that in the title of Figures 12.2 and 12.3. “true r = 0.35” and “true r = 0.65” should be corrected as “true Z_r”. The method we used here is to first generate an underlying population that has null (0), moderate (0.35), or large (0.65) Z_r correlations, then test whether the sampling distribution drawn from these populations followed a normal distribution across varying sample sizes. Nevertheless, the reviewer correctly noticed discrepancies between the reported true Z_r and its sampling distribution peak. This discrepancy arises because, when generating large population data, achieving exact values close to a strong correlation like Z_r = 0.65 is unlikely. We loop through simulations to generate population data and ensure their Z_r values fall within a threshold. For moderate effect sizes (e.g., Z_r = 0.35), this is straightforward using a narrow range (0.345 < Z_r < 0.355). However, for larger effect sizes like Z_r = 0.65, a wider range (0.6 < Z_r < 0.7) is required. therefore sometimes the population we used to draw the sample has a Z_r slightly deviated from 0.65. This remains reasonable since the main point of this analysis is to ensure that large Z_r still has a normal sampling distribution, but not focus specifically on achieving Z_r = 0.65.

We acknowledge that this variability of the range used was not clearly explained and it is not accurate to report “true Z_r = 0.65”. In the revised version, we will address this issue by adding vertical lines to each subplot to indicate the Z_r of the population we used to draw samples, making it easier to check if it aligns with the sampling peak. In addition, we will revise the title to “Sampling distributions of Z_r drawn from strong correlations (Z_r = 0.6-0.7)”. We confirmed that population Z_r and the peak of their sampling distribution remain consistent under both H0 and H1 in all sample sizes with n > 25, and we hope this explanation can fully resolve your concern.

We agree with the reviewer that claiming Z_r = -1 represents the null hypothesis is not accurate. The circlin Z_r = 0 is better analogous to Pearson’s r = 0 since both represent the mean drawn from the population with the null hypothesis. In contrast, the mean effect size under null will be positive in the raw circlin r, which is one of the important reasons for the transformation. To provide a more accurate interpretation, we will update Table 6 to describe the following strength levels of evidence: no effect (r < 0), null (r = 0), small (r = 0.1), moderate (r = 0.3), and large (r = 0.5).

eLife Assessment

This manuscript describes a fundamental investigation of the functioning of Cas9 and in particular on how variant xCas9 expands DNA targeting ability by an increase-flexibility mechanism. The authors provide compelling evidence to support their mechanistic models and the relevance of flexibility and entropy in recognition. This work can be of interest to a broad community of structural biophysicists, computational biologists, chemists, and biochemists.

Joint Public Review:

Summary:

Hossain and coworkers investigate the mechanisms of recognition of xCas9, a variant of Cas9 with expanded targeting capability for DNA. They do so by using molecular simulations and combining different flavors of simulation techniques, ranging from long classical MD simulations, to enhanced sampling, to free energy calculations of affinity differences. Through this, the authors are able to develop a consistent model of expanded recognition based on the enhanced flexibility of the protein receptor.

Strengths:

The paper is solidly based on the ability of the authors to master molecular simulations of highly complex systems. In my opinion, this paper shows no major weaknesses. The simulations are carried out in a technically sound way. Comparative analyses of different systems provide valuable insights, even within the well-known limitations of MD. Plus, the authors further investigate why xCas9 exhibits improved recognition of the TGG PAM sequence compared to SpCas9 via well-tempered metadynamics simulations focusing on the binding of R1335 to the G3 nucleobase and the DNA backbone in both SpCas9 and xCas9. In this context, the authors provide a free-energy profiling that helps support their final model.

The implementation of FEP calculations to mimic directed evolution improvement of DNA binding is also interesting, original and well-conducted.

Overall, my assessment of this paper is that it represents a strong manuscript, competently designed and conducted, and highly valuable from a technical point of view.

Weaknesses:

To make their impact even more general, the authors may consider expanding their discussion on entropic binding to other recent cases that have been presented in the literature recently (such as e.g. the identification of small molecules for Abeta peptides, or the identification of "fuzzy" mechanisms of binding to protein HMGB1). The point on flexibility helping adaptability and expansion of functional properties is important, and should probably be given more evidence and more direct links with a wider picture.

eLife Assessment

This study reports valuable findings that highlight the importance of data quality and data representation for ligand-based virtual screening experiments. The authors' claims are supported by solid evidence, although the conclusions have been inferred from only two datasets. The work would gain much impact if additional datasets were used. The main findings will be of interest to cheminformaticians and medicinal chemists working in QSAR modeling, and possibly in other areas related to machine learning.

Reviewer #1 (Public review):

Summary:

The work provides more evidence of the importance of data quality and representation for ligand-based virtual screening approaches. The authors have applied different machine learning (ML) algorithms and data representation using a new dataset of BRAF ligands. First, the authors evaluate the ML algorithms, and demonstrate that independently of the ML algorithm, predictive and robust models can be obtained in this BRAF dataset. Second, the authors investigate how the molecular representations can modify the prediction of the ML algorithm. They found that in this highly curated dataset the different molecule representations are adequate for the ML algorithms since almost all of them obtain high accuracy values, with Estate fingerprints obtaining the worst performing predictive models and ECFP6 fingerprints producing the best classificatory models. Third, the authors evaluate the performance of the models on subsets of different composition and size of the BRAF dataset. They found that given a finite number of active compounds, increasing the number of inactive compounds worsens the recall and accuracy. Finally, the authors analyze if the use of "less active" molecules affect the model's predictive performance using "less active" molecules taken from ChEMBl Database or using decoys from DUD-E. As results, they found that the accuracy of the model falls as the number of "less active" examples in the training dataset increases while the implementation of decoys in the training set generates results as good as the original models or even better in some cases. However, the use of decoys in the training set worsens the predictive power in the test sets that contain active and inactive molecules.

Strengths:

This is a highly relevant topic in medicinal chemistry and drug discovery. The manuscript is well-written, with a clear structure that facilitates easy reading, and it includes up-to-date references. The hypotheses are clearly presented and appropriately explored. The study provides valuable insights into the importance of deriving models from high-quality data, demonstrating that, when this condition is met, complex computational methods are not always necessary to achieve predictive models. Furthermore, the generated BRAF dataset offers a valuable resource for medicinal chemists working in ligand-based virtual screening.

Weaknesses:

While the work highlights the importance of using high-quality datasets to achieve better and more generalizable results, it does not present significant novelty, as the analysis of training data has been extensively studied in chemoinformatics and medicinal chemistry. Additionally, the inclusion of "AI" in the context of data-centric AI is somewhat unclear, given that the dataset curation is conducted manually, selecting active compounds based on IC50 values from ChEMBL and inactive compounds according to the authors' criteria.

Moreover, the conclusions are based on the analysis of only two high-quality datasets. To generalize these findings, it would be beneficial to extend the analysis to additional high-quality datasets (at least 10 datasets for a robust benchmarking exercise).

A key aspect that could be improved is the definition of an "inactive" compound, which remains unclear. In the manuscript, it is stated:

• "The inactives were carefully selected based on the fact that they have no known pharmacological activity against BRAF."<br /> Does the lack of BRAF activity data necessarily imply that these compounds are inactive?<br /> • "We define a compound as 'inactive' if there are no known pharmacological assays for the said compound on our target, BRAF."<br /> However, in the authors' response, they mention:<br /> • "We selected certain compounds that we felt could not possibly be active against BRAF, such as ligands for neurotransmitter receptors, as inactives."

Given that the definition of "inactive" is one of the most critical concepts in the study, I believe it should be clearly and consistently explained.

Lastly, while statistical comparison is not always common in machine learning, it would greatly enhance the value of this work, especially when comparing models with small differences in accuracy.

Reviewer #2 (Public review):

Summary:

The authors explored the importance of data quality and representation for ligand-based virtual screening approaches. I believe the results could be of potential benefit to the drug discovery community, especially to those scientists working in the field of machine learning applied to drug research. The in silico design is comprehensive and adequate for the proposed comparisons.

This manuscript by Chong A. et al describes that it is not necessary to resort to the use of sophisticated deep learning algorithms for virtual screening, since based on their results considering conventional ML may perform exceptionally well if feeded by the right data and molecular representations.

The article is interesting and well-written. The overview of the field and the warning about dataset composition are very well thought-out and should be of interest to a broad segment of the AI in drug discovery readership. This article further highlights some of the considerations that need to be taken into consideration for the implementation of data-centric AI for computer-aided drug design methods.

Strengths:

This study contributes significantly to the field of machine learning and data curation in drug discovery. The paper is, in general, well-written and structured. However, in my opinion, there are some suggestions regarding certain aspects of the data analyses.

Weaknesses:

The conclusions drawn in the study are based on the analysis of a two dataset. The authors chose BRAF as an example in this study, and expanded with BACE-1 dataset; however a benchmark with several targets would be suitable to evaluate reproducibility or transferability of the method. One concern could be the applicability of the method in other targets.

Reviewer #3 (Public review):

Summary:

The authors presented a data-centric ML approach for virtual ligand screening. They used BRAF as an example to demonstrate the predictive power of their approach.

Strengths:

The performance of predictive models in this study is superior (nearly perfect) with respect to exiting methods.

Comments on revisions:

In the revised manuscript, the presented approach has been robustly tested and can be very useful for ligand prediction.

Author response:

The following is the authors’ response to the original reviews.

We thank the Editors and reviewers for their candid evaluation of our work. While it was suggested that we should demonstrate the validity of our approach with maybe 10 different datasets but we felt that this would place an undue burden on our resources. Generally, it takes about 4 to 6 months for us to build a dataset and this does not include the time taken to train and test our AI models. This would mean that it would take us another 3 to 5 years to complete this research project if we chose to provide 10 different datasets. Publishing a research on one dataset is definitely not unheard of: for example, Subramanian et al. (2016) published their widely-cited benchmark dataset for just BACE1 inhibitors. However, we hoped that the additional work where we showed that we were able to improve the benchmark dataset for BACE1 inhibitors and achieve the same high level of predictive performance for this dataset would convince the readers (and reviewers) of the reproducibility of our approach. Furthermore, we also showed that our approach is robust and does not rely on a large volume of data to achieve this near-perfect accuracy. As can be seen in the Supplemental section, even our AI models trained on ONLY 250 BRAF actives and 250 inactives could achieve 96.3% accuracy! Logically, if the model is robust then we would expect the model to be reproducible. As such, we do not feel it is necessary for us to test our approach on 10 different datasets. 

It was also suggested that we expand this study to other types of molecular representations to give a better idea of generalizability. We would like to point out that we tested, in total, 55 single fingerprints and paired combinations. Our goal was to create an approach that could give superior performance for virtual screening and we believe that we have achieved this. Based on the results of our study, we are of the opinion that molecular representations do not, in general, have an oversized effect on AI virtual screening. Although it is important to be aware that certain molecular representations may give SLIGHTLY better performance but we can see that with the exception of the 79-bit E-State fingerprint (which could still achieve an impressive 85% accuracy for the SVM model), nearly all molecular fingerprints and paired combinations that we used were able to achieve an accuracy of above 97%. Therefore, we do not share the reviewers' concern that our approach may not be useful when applied with other types of molecular representations.

It is true that our work involved manual curation of the datasets but the goal of this paper is to lay down some  ground rules for the future development of a data-centric AI approach. Although manual curation is a routine practice in AI/ML, but it should be recognised that there is good manual curation and bad manual curation, and rules need to be established to ensure we have good manual curation. Without these rules, we would also not be able to establish and train a data-centric AI. All manual curation involves a level of subjectiveness but that subjectiveness comes from one's experience and domain knowledge of the field in which the AI is being applied. For example, in the case of this study, we relied on our knowledge and understanding of pharmacology to determine whether a compound is pharmacologically inactive or active. This may seem somewhat arbitrary to the uninitiated but it is anything but arbitrary. It is through careful thought and assessment of the chemical compounds that we choose these compounds for training the AI. Unfortunately, this sort of subjective assessment cannot be easily or completely explained but we do show where current practices have failed when building a dataset for training an AI for virtual screening.

eLife Assessment

This important study used an automated system to collect eggs laid over the course of multiple days by individual female Drosophila to successfully reveal a robust yet noisy circadian rhythm of egg-laying. Their results show that the neural control of this rhythm is entirely different from the one that controls locomotor activity rhythmicity. Preliminary connectome-based analyses provide evidence for connections between the relevant clock neurons and neurons involved in oviposition. The evidence provided is solid, although using an independent tool for targeted knockdown of clock genes and including the time series of representative individuals for all genotypes tested would help interpret the results.

Joint Public Review:

Riva et al uncovered the neural substrate underlying the oviposition rhythm in Drosophila melanogaster using a novel device that automates egg collection from individual mated females over the course of multiple days. By systematically knocking down the clock gene period in specific clock neurons the authors show that three cryptochrome (cry) positive dorso-lateral neurons (LNds) present in each hemisphere of the fly brain are critical to generating a female, sex-specific rhythm in oviposition. Interestingly, these neurons are not essential for freerunning locomotor activity. By contrast, the LNvs (lateral ventral neurons), which are essential for freerunning locomotor activity rhythmicity, were not involved in controlling the circadian rhythmicity of oviposition. Thus, this work has identified the first truly sex-specific circadian circuit in Drosophila. Using available Drosophila hemibrain connectome data they identify bidirectional connections between cry-expressing LNd and oviposition-related neurons.

Strengths:

This paper established a new semi-automatic device to register egg-laying activity, in Drosophila and found a specific role for a subset of clock neurons in the control of a female-specific circadian behavior. They also lay the groundwork for understanding how these neurons are connected to the neurons that control egg laying.

Weaknesses:

(1) Controls for the genetic background are incomplete, leaving open the possibility that the observed oviposition timing defects may be due to targeted knockdown of the period (per) gene but from the GAL4, Gal80, and UAS transgenes themselves. To resolve this issue the authors should determine the egg-laying rhythms of the relevant controls (GAL4/+, UAS-RNAi/+, etc); this only needs to be done for those genotypes that produced an arrhythmic egg-laying rhythm.

(2) Reliance on a single genetic tool to generate targeted disruption of clock function leaves the study vulnerable to associated false positive and false negative effects: a) The per RNAi transgene used may only cause partial knockdown of gene function, as suggested by the persistent rhythmicity observed when per RNAi was targeted to all clock neurons. This could indicate that the results in Fig 2C-H underestimate the phenotypes of targeted disruption of clock function. b) Use of a single per RNAi transgene makes it difficult to rule out that off-target effects contributed significantly to the observed phenotypes. We suggest that the authors repeat the critical experiments using a separate UAS-RNAi line (for period or for a different clock gene), or, better yet, use the dominant negative UAS-cycle transgene produced by the Hardin lab (https://doi.org/10.1038/22566).

(3) The egg-laying profiles obtained show clear damping/decaying trends which necessitates careful trend removal from the data to make any sense of the rhythm. Further, the detrending approach used by the authors is not tested for artefacts introduced by the 24h moving average used.

(4) According to the authors the oviposition device cannot sample at a resolution finer than 4 hours, which will compel any experimenter to record egg laying for longer durations to have a suitably long time series which could be useful for circadian analyses.

(5) Despite reducing the interference caused by manually measuring egg-laying, the rhythm does not improve the signal quality such that enough individual rhythmic flies could be included in the analysis methods used. The authors devise a workaround by combining both strongly and weakly rhythmic (LSpower > 0.2 but less than LSpower at p < 0.05) data series into an averaged time series, which is then tested for the presence of a 16-32h "circadian" rhythm. This approach loses valuable information about the phase and period present in the individual mated females, and instead assumes that all flies have a similar period and phase in their "signal" component while the distribution of the "noise" component varies amongst them. This assumption has not yet been tested rigorously and the evidence suggests a lot more variability in the inter-fly period for the egg-laying rhythm.

(6) This variability could also depend on the genotype being tested, as the authors themselves observe between their Canton-S and YW wild-type controls for which their egg-laying profiles show clearly different dynamics. Interestingly, the averaged records for these genotypes are not distinguishable but are reflected in the different proportions of rhythmic flies observed. Unfortunately, the authors also do not provide further data on these averaged profiles, as they did for the wild-type controls in Figure 1, when they discuss their clock circuit manipulations using perRNAi. These profiles could have been included in Supplementary figures, where they would have helped the reader decide for themselves what might have been the reason for the loss of power in the LS periodogram for some of these experimental lines.

(7) By selecting 'the best egg layers' for inclusion in the oviposition analyses an inadvertent bias may be introduced and the results of the assays may not be representative of the whole population.

(8) An approach that measures rhythmicity for groups of individual records rather than separate individual records is vulnerable to outliers in the data, such as the inclusion of a single anomalous individual record. Additionally, the number of individual records that are included in a group may become a somewhat arbitrary determinant for the observed level of rhythmicity. Therefore, the experimental data used to map the clock neurons responsible for oviposition rhythms would be more convincing if presented alongside individual fly statistics, in the same format as used for Figure 1.

(9) The features in the experimental periodogram data in Figures 3B and D are consistent with weakened complex rhythmicity rather than arrhythmicity. The inclusion of more individual records in the groups might have provided the added statistical power to demonstrate this. Graphs similar to those in 1G and 1I, might have better illustrated qualitative and quantitative aspects of the oviposition rhythms upon per knockdown via MB122B and Mai179; Pdf-Gal80.

Wider context:

The study of the neural basis of oviposition rhythms in Drosophila melanogaster can serve as a model for the analogous mechanisms in other animals. In particular, research in this area can have wider implications for the management of insects with societal impact such as pests, disease vectors, and pollinators. One key aspect of D. melanogaster oviposition that is not addressed here is its strong social modulation (see Bailly et al.. Curr Biol 33:2865-2877.e4. doi:10.1016/j.cub.2023.05.074). It is plausible that most natural oviposition events do not involve isolated individuals, but rather groups of flies. As oviposition is encouraged by aggregation pheromones (e.g., Dumenil et al., J Chem Ecol 2016 https://link.springer.com/article/10.1007/s10886-016-0681-3) its propensity changes upon the pre-conditioning of the oviposition substrates, which is a complication in assays of oviposition rhythms that periodically move the flies to fresh substrate.

Author response:

(1) Controls for the genetic background are incomplete, leaving open the possibility that the observed oviposition timing defects may be due to targeted knockdown of the period (per) gene but from the GAL4, Gal80, and UAS transgenes themselves. To resolve this issue the authors should determine the egg-laying rhythms of the relevant controls (GAL4/+, UAS-RNAi/+, etc); this only needs to be done for those genotypes that produced an arrhythmic egg-laying rhythm.

We agree with this objection, and in the corrected version we plan to provide the assessment of the egg laying rhythms for the missing GAL4 controls as recommended only for Figure 3.

(2) Reliance on a single genetic tool to generate targeted disruption of clock function leaves the study vulnerable to associated false positive and false negative effects: a) The per RNAi transgene used may only cause partial knockdown of gene function, as suggested by the persistent rhythmicity observed when per RNAi was targeted to all clock neurons. This could indicate that the results in Fig 2C-H underestimate the phenotypes of targeted disruption of clock function. b) Use of a single per RNAi transgene makes it difficult to rule out that off-target effects contributed significantly to the observed phenotypes. We suggest that the authors repeat the critical experiments using a separate UAS-RNAi line (for period or for a different clock gene), or, better yet, use the dominant negative UAS-cycle transgene produced by the Hardin lab (https://doi.org/10.1038/22566).

We have recently acquired mutant flies with a dominant negative-cycle transgene (UAS-cycDN, Tanoue et al. 2004), and we plan to repeat our experiments with these mutants, in order to confirm our results.

(3) The egg-laying profiles obtained show clear damping/decaying trends which necessitates careful trend removal from the data to make any sense of the rhythm. Further, the detrending approach used by the authors is not tested for artefacts introduced by the 24h moving average used.

In the revised version we will show that the detrending approach used does not introduce any artefacts. The analysis of numerical simulations with an aperiodic stochastic signal superposed to a decaying signal shows that the detrending method used does not result in a spurious periodic signal. Furthermore, we can show that when the underlying signal is rhythmic, the correct period is obtained even when the moving average is a few hours larger or smaller than 24 h.

(4) According to the authors the oviposition device cannot sample at a resolution finer than 4 hours, which will compel any experimenter to record egg laying for longer durations to have a suitably long time series which could be useful for circadian analyses.

We apologize for not being clear enough. The device can in principle sample at any desired resolution. Notice, however, that the variable we are analyzing (number of eggs laid by a single female) has only a few possible values, which is one of the features that render the assessment of rhythmicity a particularly difficult task. If egg laying is sampled more often (say, at 2 h intervals) more time points will be available, but the values available for each time point will be much less. We will show an example where we compare both rates (2h and 4h). Even though the 2h sampling reveals the rhythmicity of the time series, the significance of the peaks obtained is less than when sampling at 4h intervals. We have found that a 4h sampling seems to provide the best compromise between frequency of the sampling and discreteness of the variable.

On the other hand, it is important to stress that sampling frequency and longer durations are not very correlated (see e.g. Cohen et al. Journal of Theoretical Biology 314, pp 182 [2012]). It has been shown that the best way to make accurate predictions of the period of a rhythmic signal is to have a series spanning many cycles, irrespective of the sampling frequency. In other words, it is not true that with a 2h sampling it would be possible to analyze shorter series than with 4h sampling. Unfortunately, egg laying records are usually less than 5 cycles long, which is one of the reasons for the difficulties in the assessment of their rhythmicity.

(5) Despite reducing the interference caused by manually measuring egg-laying, the rhythm does not improve the signal quality such that enough individual rhythmic flies could be included in the analysis methods used. The authors devise a workaround by combining both strongly and weakly rhythmic (LSpower > 0.2 but less than LSpower at p < 0.05) data series into an averaged time series, which is then tested for the presence of a 16-32h "circadian" rhythm. This approach loses valuable information about the phase and period present in the individual mated females, and instead assumes that all flies have a similar period and phase in their "signal" component while the distribution of the "noise" component varies amongst them. This assumption has not yet been tested rigorously and the evidence suggests a lot more variability in the inter-fly period for the egg-laying rhythm.

The assumption is difficult to test rigorously, since for individual flies the records seem to be so noisy that no information can be extracted. As shown in the paper, it is even very difficult to assess the presence of rhythmicity at the individual level. We consider that the appearance of a rhythm after averaging several records shows the presence of this rhythm at the individual level. But it could be argued that the presence of rhythmicity in the average record could be due to only a few (or even a single) rhythmic individuals. In order to show that this is probably not the case, in the revised version we will show that, when the individuals that are rhythmic are left out, the average of the remaining flies still shows a rhythm (albeit a weaker one, as was to be expected).

Regarding our assumption that all flies have the “same” period, the results on Fig. 1 F cannot really rule out this possibility, because with so few cycles, the determination of the period is not very accurate (see e.g. Cohen et al. Journal of Theoretical Biology 314, pp 182 [2012]). In our case, the error for the period is related to the width of the corresponding peak in the periodogram, which is typically 4 hs. In any case, in the revised version we will try to show, by using numerical simulations, that when the individual periods are not the same, but are distributed approximately as in Fig 1F, the average series is still rhythmic with the correct period.

(6) This variability could also depend on the genotype being tested, as the authors themselves observe between their Canton-S and YW wild-type controls for which their egg-laying profiles show clearly different dynamics. Interestingly, the averaged records for these genotypes are not distinguishable but are reflected in the different proportions of rhythmic flies observed. Unfortunately, the authors also do not provide further data on these averaged profiles, as they did for the wild-type controls in Figure 1, when they discuss their clock circuit manipulations using perRNAi. These profiles could have been included in Supplementary figures, where they would have helped the reader decide for themselves what might have been the reason for the loss of power in the LS periodogram for some of these experimental lines.

Even though we think that the individual records are in general too noisy to be really informative, we will provide all the individual egg profiles in the Supplementary Material of the revised version, in order to let the reader, check this for herself/himself.

(7) By selecting 'the best egg layers' for inclusion in the oviposition analyses an inadvertent bias may be introduced and the results of the assays may not be representative of the whole population.

We agree that this may introduce some bias in the results. But in our opinion this bias is very difficult to avoid, since for females that lay very few eggs, rhythmicity can even be difficult to define (some females can spend a whole day without laying a single egg). On the other hand, even when the results may not be representative of the whole population, they would be representative of the flies that lay most of the eggs in a population, which seems to be very relevant in ecological terms.

(8) An approach that measures rhythmicity for groups of individual records rather than separate individual records is vulnerable to outliers in the data, such as the inclusion of a single anomalous individual record. Additionally, the number of individual records that are included in a group may become a somewhat arbitrary determinant for the observed level of rhythmicity. Therefore, the experimental data used to map the clock neurons responsible for oviposition rhythms would be more convincing if presented alongside individual fly statistics, in the same format as used for Figure 1.

The question of possible rhythmic outliers has been addressed above, in question 5, where we discuss why we think that such outliers are not “determinant for the observed level of rhythmicity”. As also mentioned above, even though we think that they are too noisy to be informative, we plan to include all individual profiles in the Supplementary Material.

(9) The features in the experimental periodogram data in Figures 3B and D are consistent with weakened complex rhythmicity rather than arrhythmicity. The inclusion of more individual records in the groups might have provided the added statistical power to demonstrate this. Graphs similar to those in 1G and 1I, might have better illustrated qualitative and quantitative aspects of the oviposition rhythms upon per knockdown via MB122B and Mai179; Pdf-Gal80.

We assume that the features mentioned refer to the appearance in the periodograms of two small peaks under the significance lines. We are aware that in the studies of the rhythmicity of locomotor activity such features are usually interpreted as “complex rhythms”, i.e. as evidence of the existence of two different mechanisms producing two different rhythms in the same individual. In our case, however, at least two other possibilities should be taken into account. Since the periodograms we show assess the rhythmicity of the average time series of several individuals, the two small peaks could correspond to the periods of two different subpopulations. Another possibility could be that such peaks are simply an artifact of the method in the analysis of time series that consist of very few cycles (as explained above) and also few points per cycle. A cursory examination of the individual profiles, that will be provided in the new version, do not seem to support any of the first two possibilities mentioned. On the other hand, we will show evidence that the analysis of series that are perfectly random sometimes result in periodograms with some small peaks.

eLife Assessment

This important study demonstrates the ability for high-throughput recording and categorization of unconstrained and stimulus-based behaviors across a very large population of marmosets (n = 120 animals across 36 family units). The authors implement an analytical approach to identify "outlier" behavior that could be key in the development of next-generation precision psychiatry. While the strength of evidence appears solid overall, many key methodological details are incomplete.

Reviewer #1 (Public review):

Summary:

The authors demonstrate a fully unsupervised, high throughput (meaning very low human interaction required) approach to quantifying marmoset behavior in unconstrained environments.

Strengths:

The authors provide an approach that is scalable, easy to implement at face value, and highly robust. Currently, most behavioral quantification approaches do not work well on marmosets, or the published examples that do look promising do not scale towards high throughput as demonstrated by the authors.

While marmosets can certainly be a useful translational research model devoid of free behavior quantification, the authors make a compelling point about how this approach can be useful in the study of treatments of emerging marmoset disease models.

Overall this is a very exhaustive manuscript that overcomes significant shortcomings in previous work and speaks highly to the use of marmosets for unconstrained behavioral and neural assessment.

Weaknesses:

Recording marmoset behavior with a 60Hz frame rate is a significant limitation to the approach which is hopefully easily alleviated in the future through better cameras/reconstruction pipelines. Marmosets (in the reviewers' experience) have a lot of motion energy above the 30Hz nyquist limit imposed by this system and are agile to a degree requiring higher frame rates.

The manuscript neglects recent approaches to non-human primate behavioral quantification from other groups that should be included. Simians are simians after all.

As a minor weakness, this reviewer would have liked to see code shared for the reviewers to evaluate, especially pertaining to the high throughput and robustness of the approach.

Reviewer #2 (Public review):

In this manuscript, Menegas et al. classify the "control" behavior of captive marmosets. They combine behavioral screening from video recordings with audio and neural recordings (from the striatum) to better define what can be considered a typical behavioral repertoire for captive marmoset monkeys. A range of analyses is presented, investigating various aspects of behavior, such as social interactions and the detection of atypical individuals.

The manuscript is compelling in many respects, especially due to the richness of the dataset and the breadth of analyses presented. However, a significant issue with the manuscript lies in its writing: the results are conveyed in an overly succinct and superficial manner, and the "Methods" section is nearly absent. Key concepts are often undefined, and the mathematical details underlying the figures are not explained, leaving readers to guess the authors' approach.

Another issue is the vague use of the term "natural behavior." All data presented here appear to have been collected in small cages with limited climbing opportunities and enrichment. Thus, the authors should refrain from using "natural" to describe these conditions.

Below, we elaborate further on the lack of methodological detail. Based on these issues, we believe the manuscript, in its current form, does not meet the scientific standards necessary for proper review. We strongly encourage the authors to undertake an extensive revision.

Major Revision Points:

The methods and results require significantly more detail. A scientific publication should provide readers with enough information to reproduce the study. Here, the detail level is far too low to fully understand, or reproduce, the study, and in many instances, readers are left to guess how the figure panels were produced. Below is a non-exhaustive list of examples illustrating these issues:

(1) "we temporarily placed horizontal cage dividers to reduce the total cage size during data collection": What were the resulting (and initial) cage dimensions?

(2) "After training the network, we hierarchically clustered the latent space": What is the latent space? Based on Figure 2a, it appears related to the network's recurrent layer, but this is not clarified in the text.

(3) Alpha and perplexity parameters: Please define these terms. Since these concepts appear fundamental, readers should not have to consult external references.

(4) "We then traced cluster identities across hierarchical levels": What are hierarchical levels?

(5) "To understand how the input time series data was weighed in the bottleneck layer of the model": What is the bottleneck layer?

(6) "we measured the average attention allocation to previous time points": The authors should define "attention allocation."

(7) "we compared each neuron's firing rate distribution to shuffled data based on the overall frequency of each behavior during the session": This description is insufficient to understand the analysis.

(8) "we hierarchically clustered neurons according to their firing rate enrichment maps": No mathematical explanation is provided for neuron clustering, nor is the concept of a "firing rate enrichment map" clarified.

(9) "Cluster 4 showed higher activity when neurons were 'alone' or 'active'": This is vague and uses unclear jargon (e.g., "neurons alone"). Additionally, no mathematical explanation is provided for assigning neuronal activity to behavioral states.

(10) Figure 3f, right-side panels: The analysis seems to involve cage mate positioning, yet no description is provided.

(11) "we used motion watches to measure activity across all hours": Are these motion-sensitive watches physically attached to the animals? The methodology should be described, including data analysis details.

This list could continue, but we trust the authors understand the point. There is a wealth of analyses and information in this study, but the descriptions are too superficial. We understand that fully describing each analysis may require significant rewriting, including supplementary figures, and will likely make the manuscript longer. This is entirely acceptable, as the ideas presented here are worth the added rigor.

"Natural behavior": Typically, the term "natural" suggests that the dataset reflects the range of behaviors exhibited by animals in the wild. Here, however, recordings were made in a small cage with limited climbing opportunities and enrichment. Under these conditions, it's hard to justify describing the behavior as "natural". In a project aimed at classifying the behavioral repertoire of marmoset monkeys and making this dataset accessible to other laboratories, it would be helpful to include more detailed information about the animals' housing conditions. This might include cage sizes, temperature, humidity, and details on food quantities, quality, and feeding times.

Correlation versus causation: In the section titled "Large-scale data collection reveals variability across days and correlation between cagemates," the authors conclude: "Overall, these results indicate that measurements of animals' behavioral traits depend heavily on their social environment." This interpretation seems incorrect. We know that animal behavior varies throughout the day, with activity peaks typically occurring in the morning and afternoon. Such factors, or other external influences, could induce correlations between animals that are not caused by social interactions.

Figure 4g: What are we intended to conclude from this analysis?

Figure 5: Please specify the type of calls analyzed. For example, did you analyze only long-distance calls (aka 'loud phees' or 'shrills')? In "We split the audio data into 5-minute (non-continuous) segments and found that the average call rate in these segments varied from 0 calls per minute to 60 calls per minute (Fig. 5d-e)," does the call rate refer to individual animals or the entire cage?

"This implies that a high rate of calls in a room can interrupt animals during social resting states and cause them to preferentially exhibit more active/attentive states." Does it? This could simply indicate that more active animals produce more calls.

"We recorded neural activity in the striatum because it is known to contain diverse signals related to movement and social interactions." While I understand that the authors intend to publish neural data separately, a brief discussion of the striatum's role here would be helpful.

Author response:

We would like to thank the editors and reviewers for taking the time to help improve our manuscript. We appreciate the feedback and will definitely increase the level of methodological detail in a revised submission.

Here is a brief summary of our plan to address the points raised by the reviewers. We will respond to the comments in a point-by-point manner when we resubmit a revised manuscript.

Reviewer 1

This reviewer raised a question about the 60 Hz frame rate for recording. We agree that increasing the number of cameras and frame rate would improve the tracking quality, but this would come at the cost of scalability. In the current study (and other concurrent studies in the lab), we recorded from 10-20 families simultaneously to try to sample the distribution of behavioral responses to stimuli observed in animals in our colony. This was only possible logistically because of the lightweight equipment design allowing us to record data from animals without large disruptions to their home-cage environment.

One strategy for acquiring higher-resolution data is to build a small number of enclosures that are fully surrounded by cameras, and to cycle animals through these enclosures (1). However, this strategy limits throughput by reducing the number of animals per day that can be studied. If the size and cost of cameras and computers decreases in the future, then this recording strategy will be scalable to the whole-colony level. For our current study and analysis, we are limited by the resolution of our dataset. We do believe that our data (although not a perfect 3d reconstruction or an extremely high frame rate) is sufficient to label behavioral states with high accuracy. We will add a figure to more clearly show that behavioral state data can be accurately inferred from this imperfect data, which has also been recently highlighted by other groups (2).

Additionally, with recent progress in the application of deep learning to animal pose tracking, new models can infer 3d pose dynamics from 2d data (3) and leverage spatiotemporal structure to clean up noisy data (4). We believe that other groups will be able to use these types of approaches to extract much more value from this dataset. So, in summary, we do understand the concern related to reconstruction quality and will 1) more clearly define the usefulness of our current models, 2) release our data and code so that others can build upon it or repurpose it, and 3) plan future experiments with higher camera count and frame rate as permitted by logistical constraints. 

Reviewer 2

This reviewer asked for an increased level of methodological detail. We will try to address this in a few ways:

(1) Code and data sharing. We believe that many of the questions related to the methodology will be best answered by sharing the data and code directly. Because there is a large amount of code associated with this manuscript, it is impractical to list every step and every parameter in the paper. Along with our revised manuscript, we will make our data and code publicly available. That said, we will improve our description of key parameters in the paper as the reviewer suggested.

(2) More detailed Methods section. The reviewer asked us to provide more methodological detail. We understand that this is currently a weakness of our manuscript, and we will focus on addressing it. For instance, the reviewer rightly points out that we did not describe the motion watches used to generate the data in Figure S7. We will address this.

(3) Simplify the manuscript. The paper currently has 22 figures, and further analysis could be done based on the results shown in any of them. For instance, this reviewer asked us to add a comparison across females and males (similar to our comparison of juveniles and adults). While we plan to add that analysis, we recognize that there are several figures/panels that are not closely related to our intended goal of describing the patterns we found in our large dataset. We will simplify the manuscript by removing some excess figures/panels and focus on describing the parts of the analysis that are crucial to our conclusions in greater detail.

(4) More careful language. This reviewer pointed out that there were some inaccuracies with our descriptive language. For instance, we used the term "natural" behavior to describe the behavior of animals in captivity, which may more accurately be described as their home-cage behavior. We will be more careful to align our language to the standard for the field. For instance, several studies refer to unrestrained behavior in a laboratory setting as "spontaneous" behavior rather than "natural" behavior (5). In our case, the data consists of both spontaneously occurring behavior and responses to a set of stimuli. We will make sure that the descriptions are more precise in the revised manuscript.

(1) Bala, P. C. et al. Automated markerless pose estimation in freely moving macaques with OpenMonkeyStudio. Nat Commun 11, (2020).

(2) Weinreb, C. et al. Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics. bioRxiv (2023) doi:10.1101/2023.03.16.532307.

(3) Gosztolai, A. et al. LiftPose3D, a deep learning-based approach for transforming two-dimensional to three-dimensional poses in laboratory animals. Nat Methods 18, 975–981 (2021).

(4) Wu, A. et al. Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking. Adv Neural Inf Process Syst 33, 6040–6052 (2020).

(5) Levy, D. R. et al. Mouse spontaneous behavior reflects individual variation rather than estrous state. Curr Biol 33, 1358-1364.e4 (2023).

eLife Assessment

MGPfactXMBD is a novel computational method for investigating cell evolutionary trajectory for scRNA-seq samples. It is important, with several potential future applications. The authors benchmarked this method using synthetic and real-world samples and showed superior performance for some of the tasks in cell trajectory analysis compared to other methods with compelling evidence.

Reviewer #1 (Public review):

Summary:

Ren et al developed a novel computational method to investigate cell evolutionary trajectory for scRNA-seq samples. This method, MGPfact, estimates pseudotime and potential branches in the evolutionary path through explicitly modeling the bifurcations in a Gaussian process. They benchmarked this method using synthetic as well as real world samples and showed superior performance for some of the tasks in cell trajectory analysis. They further demonstrated the utilities of MGPfact using single cell RNA-seq samples derived from microglia or T cells and showed that it can accurately identify the differentiation timepoint and uncover biologically relevant gene signatures.

Strengths:

Overall I think this is a useful new tool that could deliver novel insights for the large body of scRNA-seq data generated in the public domain. The manuscript is written is a logical way and most parts of the method are well described.

Comments on revisions:

In this revision, the authors have sufficiently addressed all of my concerns. I don't have any follow-up comments.

Reviewer #2 (Public review):

Summary of the manuscript:

Authors present MGPfactXMBD, a novel model-based manifold-learning framework designed to address the challenges of interpreting complex cellular state spaces from single-cell RNA sequences. To overcome current limitations, MGPfactXMBD factorizes complex development trajectories into independent bifurcation processes of gene sets, enabling trajectory inference based on relevant features. As a result, it is expected that the method provides a deeper understanding of the biological processes underlying cellular trajectories and their potential determinants.

MGPfactXMBD was tested across 239 datasets, and the method demonstrated similar to slightly superior performance in key quality-control metrics to state-of-the-art methods. When applied to case studies, MGPfactXMBD successfully identified critical pathways and cell types in microglia development, validating experimentally identified regulons and markers. Additionally, it uncovered evolutionary trajectories of tumor-associated CD8+ T cells, revealing new subtypes with gene expression signatures that predict responses to immune checkpoint inhibitors in independent cohorts.

Overall, MGPfactXMBD represents a relevant tool in manifold-learning for scRNA-seq data, enabling feature selection for specific biological processes and enhancing our understanding of the biological determinants of cell fate.

Summary of the outcome:

The novel method addresses core state-of-the-art questions in biology related to trajectory identification. The design and the case studies are of relevance.

Comments on revisions:

The authors have addressed all my previous comments to satisfaction.

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1:

Comment#1: Ren et al developed a novel computational method to investigate cell evolutionary trajectory for scRNA-seq samples. This method, MGPfact, estimates pseudotime and potential branches in the evolutionary path by explicitly modeling the bifurcations in a Gaussian process. They benchmarked this method using synthetic as well as real-world samples and showed superior performance for some of the tasks in cell trajectory analysis. They further demonstrated the utilities of MGPfact using single-cell RNA-seq samples derived from microglia or T cells and showed that it can accurately identify the differentiation timepoint and uncover biologically relevant gene signatures. Overall I think this is a useful new tool that could deliver novel insights for the large body of scRNA-seq data generated in the public domain. The manuscript is written in a logical way and most parts of the method are well described.

Thank you for reviewing our manuscript and for your positive feedback on MGPfact. We are pleased that you find it useful for identifying differentiation timepoints and uncovering gene signatures. We will continue to refine MGPfact and explore its applications across diverse datasets. Your insights are invaluable, and we appreciate your support.

Comment#2: Some parts of the methods are not clear. It should be outlined in detail how pseudo time T is updated in Methods. It is currently unclear either in the description or Algorithm 1.

Thanks to the reviewers' comments. We've added a description of how pseudotime T is obtained between lines 138 and 147 in the article. In brief, the pseudotime of MGPfact is inferred through Gaussian process regression on the downsampled single-cell transcriptomic data. Specifically, T is treated as a continuous variable representing the progression of cells through the differentiation process. We describe the relationship between pseudotime and expression data using the formula:

Where f(T) is a Gaussian Process (GP) with covariance matrix S, and represents the error term. The Gaussian process is defined as:

Where is the variance set to 1e-6.

During inference, we update the pseudotime by maximizing the posterior likelihood. Specifically, the posterior distribution of pseudotime T can be represented as:

Where is the likelihood function of the observed data Y*, and is the prior distribution of the Gaussian process. This posterior distribution integrates the observed data with model priors, enabling inference of pseudotime and trajectory simultaneously. Due to the high autocorrelation of  in the posterior distribution, we use Adaptive Metropolis within Gibbs (AMWG) sampling (Roberts and Rosenthal, 2009; Tierney, 1994). Other parameters are estimated using the more efficient SLICE sampling technique (Neal, 2003).

Comment#3: There should be a brief description in the main text of how synthetic data were generated, under what hypothesis, and specifically how bifurcation is embedded in the simulation.

Thank you for the reviewers' comments. We have added descriptions regarding the synthetic dataset in the methods section. The revised content is from line 487 to 493:

The synthetic datasets were generated using four simulators: dyngen (Saelens et al., 2019), dyntoy (Saelens et al., 2019), PROSSTT (Papadopoulos et al., 2019), and Splatter (Zappia et al., 2017), each modeling different trajectory topologies such as linear, branching, and cyclic. Splatter simulates branching events by setting expression states and transition probabilities, dyntoy generates random expression gradients to reflect dynamic changes, and dyngen focuses on complex branching structures within gene regulatory networks.

Comment#4: Please explain what the abbreviations mean at their first occurrence.

We appreciate the reviewers' feedback. We have thoroughly reviewed the entire manuscript and made sure that all abbreviations have had their full forms provided upon their first occurrence.

Comment#5: In the benchmark analysis (Figures 2/3), it would be helpful to include a few trajectory plots of the real-world data to visualize the results and to evaluate the accuracy.

We appreciate the reviewer's feedback. To more clearly demonstrate the performance of MGPfact, we selected three representative cases from the dataset for visual comparison. These cases represent different types of trajectory structures: linear, bifurcation, and multifurcation. The revised content is between line 220 and 226.

As shown in Supplementary Fig. 5, it is evident that MGPfact excels in capturing main developmental paths and identifying key bifurcation points. In the linear trajectory structure, MGPfact accurately predicted the linear structure without bifurcation events, showing high consistency with the ground truth (overall\=0.871). In the bifurcation trajectory structure, MGPfact accurately captured the main bifurcation event ( ). In the multifurcation trajectory structure, although MGPfact predicted only one bifurcation point, its overall structure remains close to the ground truth, as evidenced by its high overall score (overall\=0.566). Overall, MGPfact demonstrates adaptability and accuracy in reconstructing various types of trajectory structures.

Comment#6: It is not clear how this method selects important genes/features at bifurcation. This should be elaborated on in the main text.

Thanks to the reviewers' comments. To enhance understanding, we've added detailed descriptions of gene selection in the main text and appendix, specifically from lines 150 to 161. In brief, MGPfact employs a Gaussian process mixture model to infer cell fate trajectories and identify independent branching events. We calculate load matrices using formulas 1 and 14 to assess each gene's contribution to the trajectories. Genes with an absolute weight greater than 0.05 are considered predominant in specific branching processes. Subsequently, SCENIC (Aibar et al., 2017; Bravo González-Blas et al., 2023) analysis was conducted to further infer the underlying regulons and annotate the biological processes of these genes.

Comment#7: It is not clear how survival analysis was performed in Figure 5. Specifically, were critical confounders, such as age, clinical stage, and tumor purity controlled?

To evaluate the predictive and prognostic impacts of the selected genes, we utilized the Cox multivariate regression model, where the effects of relevant covariates, including age, clinical stage, and tumor purity, were adjusted. We then conducted the Kaplan-Meier survival analysis again to ensure the reliability of the results. The revisions mainly include the following sections:

(1) We modified the description of adjusting for confounding factors in the survival analysis, from line 637 to 640:

To adjust for possible confounding effects, the relevant clinical features including age, sex and tumor stage were used as covariates. The Cox regression model was implemented using R-4.2 package “survival”. And we generated Kaplan-Meier survival curves based on different classifiers to illustrate differences in survival time and report the statistical significance based on Log-rank test.

(2) We updated the images in the main text regarding the survival analysis, including Fig. 5a-b, Fig. 6c, and Supplementary Fig. 8e.

Comment#8: I recommend that the authors perform some sort of 'robustness' analysis for the consensus tree built from the bifurcation Gaussian process. For example, subsample 80% of the cells to see if the bifurcations are similar between each bootstrap.

We appreciate the reviewers' feedback. We performed a robustness analysis of the consensus tree using 100 training datasets. This involved sampling the original data at different proportions, and then calculating the topological similarity between the consensus trajectory predictions of MGPfact and those without sampling, using the Hamming-Ipsen-Mikhailov ( ) metric. A higher score indicates greater robustness. The relevant figure is in Supplementary Fig. 4, and the description is in the main text from line 177 to 182.

The results indicate that the consensus trajectory predictions based on various sampling proportions of the original data maintain a high topological similarity with the unsampled results (HIM_mean=0.686). This demonstrates MGPfact’s robustness and generalizability under different data conditions, hence the capability of capturing bifurcative processes in the cells’ trajectory.

Reviewer #2:

Comment#1: The authors present MGPfact^XMBD, a novel model-based manifold-learning framework designed to address the challenges of interpreting complex cellular state spaces from single-cell RNA sequences. To overcome current limitations, MGPfact^XMBD factorizes complex development trajectories into independent bifurcation processes of gene sets, enabling trajectory inference based on relevant features. As a result, it is expected that the method provides a deeper understanding of the biological processes underlying cellular trajectories and their potential determinants. MGPfact^XMBD was tested across 239 datasets, and the method demonstrated similar to slightly superior performance in key quality-control metrics to state-of-the-art methods. When applied to case studies, MGPfact^XMBD successfully identified critical pathways and cell types in microglia development, validating experimentally identified regulons and markers. Additionally, it uncovered evolutionary trajectories of tumor-associated CD8+ T cells, revealing new subtypes with gene expression signatures that predict responses to immune checkpoint inhibitors in independent cohorts. Overall, MGPfact^XMBD represents a relevant tool in manifold learning for scRNA-seq data, enabling feature selection for specific biological processes and enhancing our understanding of the biological determinants of cell fate.

Thank you for your thoughtful review of our manuscript. We are thrilled to hear that you find MGPfact^XMBD beneficial for exploring cellular evolutionary paths in scRNA-seq data. Your insights are invaluable, and we look forward to incorporating them to further enrich our study. Thank you once again for your support and constructive feedback.

Comment#2: How the methods compare with existing Deep Learning based approaches such as TIGON is a question mark. If a comparison would be possible, it should be conducted; if not, it should be clarified why.

We appreciate the reviewer's comments. We have added a comparison with the sctour (Li, 2023) and TIGON methods (Sha, 2024).

It is important to note that the encapsulation and comparison of MGPfact are based on traditional differentiation trajectory construction. Saelens et al. established a systematic evaluation framework that categorizes differentiation trajectory structures into topological subtypes such as linear, bifurcation, multifurcation, graph, and tree, focusing on identifying branching structures in the cell differentiation process (Saelens et al., 2019). The sctour and TIGON methods mentioned by the reviewer are primarily used for estimating RNA velocity, focusing on continuous temporal evolution rather than explicit branching structures, and do not explicitly model branches. Therefore, we considered the predictions of these two methods as linear trajectories and compared them with MGPfact. While scTour explicitly estimates pseudotime, TIGON uses the concept of "growth," which is analogous to pseudotime, so we made the necessary adaptations.

Author response image 1 show that within this framework, compared to scTour (overall_mean=0.448) and TIGON (overall_mean=0.263), MGPfact still maintains a relatively high standard (overall_mean=0.534). This indicates that MGPfact has a significant advantage in accurately capturing branching structures in cell differentiation, especially in applications where explicit modeling of branches is required.

Author response image 1.

Comparison of MGPfact with scTour and TIGON in trajectory inference performance across 239 test datasets. a. Overall scores；b.F1_branches； c.HIM；e. wcor_features. All results are color-coded based on the trajectory types, with the black line representing the mean value. The “Overall” assessment is calculated as the geometric mean of all four metrics.

Comment#3: Missing Methods:

- The paper lacks a discussion of Deep Learning approaches for bifurcation analysis. e.g. scTour, Tigon.

- I am missing comments on methods such CellRank, and alternative approaches to delineate a trajectory.

We thank the reviewer for these comments.

(1) As mentioned in response to Comments#2, the scTour and TIGON methods are primarily used for estimating RNA velocity, focusing on continuous temporal evolution rather than explicit branching structures, and they do not explicitly model branches. We consider the predictions of these two methods as linear trajectories and compare them with MGPfact. The relevant description and discussion have been addressed in the response.

(2) We have added a description of RNA velocity estimation methods (scTour, TIGON, CellRank) in the introduction section. The revised content is from line 66 to 71:

Moreover, recent studies based on RNA velocity has provided insights into cell state transitions. These methods measure RNA synthesis and degradation rates based on the abundance of spliced and unspliced mRNA, such as CellRank (Lange et al., 2022). Nevertheless, current RNA velocity analyses are still unable to resolve cell-fates with complex branching trajectory. Deep learning methods such as scTour (Li, 2023) and TIGON (Sha, 2024) circumvent some of these limitations, offering continuous state assumptions or requiring prior cell sampling information.

Comment#4: Impact of MURP:

The rationale for using MURP is well-founded, especially for trajectory definition. However, its impact on the final results needs evaluation.

How does the algorithm compare with a random subselection of cells or the entire cell set?

Thank you for the comments. We fully agree that MURP is crucial in trajectory prediction. As a downsampling method, MURP is specifically designed to address noise issues in single-cell data by dividing the data into several subsets, thereby maximizing noise reduction while preserving the main structure of biological variation (Ren et al., 2022). In MGPfact, MURP typically reduces the data to fewer than 100 downsampled points, preserving the core biological structure while lowering computational complexity. To assess MURP's impact, we conducted experiments by randomly selecting 20, 40, 60, 80, and 100 cells for trajectory inference. These results were mapped back to the original data using the KNN graph structure for final predictions, which were then compared with the MURP downsampling results. Supplementary results can be found in Supplementary Fig. 3, with additional descriptions in the main text from line 170 to 176.

The results indicate that trajectory inference using randomly sampled cells has significantly lower prediction accuracy compared to that using MURP. This is particularly evident in branch assignment (F1_branches) and correlation cor_dist, where the average levels decrease by 20.5%-64.9%. In contrast, trajectory predictions using MURP for downsampling show an overall score improvement of 5.31%-185%, further highlighting MURP's role in enhancing trajectory inference within MGPfact.

Comment#5: What is the impact of the number of components selected?

Thank you for the comments. In essence, MGPfact consists of two main steps: 1) trajectory inference; 2) calculation of factorized scores and identification of high-weight genes. After step 1, MGPfact estimates parameters such as pseudotime T and bifurcation points B.  In step 2, we introduce a rotation matrix to obtain factor scores  for each trajectory  by rotating Y*.

For all trajectories,

where  is the error term for the -th trajectory. The number of features in Y* must match the dimensions of the rotation matrix R to ensure the factorized score matrix W contains factor scores for  trajectories, achieving effective feature representation and interpretation in the model.

Additionally, to further illustrate the impact of the number of principal components (PCs) on model performance in step 1, we conducted additional experiments. We used 3 PCs as the default and adjusted the number to evaluate changes from this baseline. As shown in Author response image 2, setting the number of PCs to 1 significantly decreases the overall performance score (overall_mean=0.363), as well as the wcor_features and cor_dist metrics.  In contrast, increasing the number of PCs does not significantly affect the metrics. It ought to be mentioned that number of components used should be determined by the intrinsic biological characteristics of the cell fate-determination. Our experiment based on a limited number of datasets may not represent more complex scenarios in other cell types.

Author response image 2.

Robustness testing of the number of MURP PCA components on 100 training datasets. With the number of principal components (PCs) set to 3 by default; we tested the impact of different number of components (1-10) on the prediction results. In all box plots, the asterisk represents the mean value, while the whiskers extend to the farthest data points within 1.5 times the interquartile range. Significance is denoted as follows: not annotated indicates non-significant; * P < 0.05; ** P < 0.01; *** P < 0.001; two-sided paired Student’s T-tests.

Comment#6: Please comment on the selection of the kernel functions (rbf and polynomial) and explain why other options were discarded.

Thank you for the comments. We have added a description regarding the selection of radial basis functions and polynomial kernels in lines 126-130. As the reviewers mentioned, the choice of kernel functions is crucial in the MGPfact analysis pipeline for constructing the covariance matrix of the Gaussian process. We selected the radial basis function (RBF) kernel and the polynomial kernel to balance capturing data complexity and computational efficiency. The RBF kernel is chosen for its ability to effectively model smooth functions and capture local variations in the data, making it well-suited to the continuous and smooth characteristics of biological processes; its hyperparameters offer modeling flexibility. The polynomial kernel is used to capture more complex nonlinear relationships between input features, with its hyperparameters also allowing further customization of the model. In contrast, other complex kernels, such as Matérn or spectral kernels, were omitted due to their interpretability challenges and the risk of overfitting with limited data. However, as suggested by the reviewers, we will consider and test the impact of other kernel functions on the covariance matrix of the Gaussian process and their role in trajectory inference in our subsequent phases of algorithm design.

Comment#7: What is the impact of the Pseudotime method used initially? This section should be expanded with clear details on the techniques and parameters used in each analysis.

We are sorry for the confusion. We've added a description of how pseudotime  is obtained between line 138 and 147 in the main text. And the specific hyperparameters involved in the model and their prior settings are detailed in the supplementary information.

In brief, the pseudotime and related topological parameters of the bifurcative trajectories in MGPfact are inferred by Gaussian process regression from downsampled single-cell transcriptomic data (MURP). Specifically,  is treated as a continuous variable representing the progression of cells through the differentiation process. We describe the relationship between pseudotime and expression data as:

where  is a Gaussian Process (GP) with covariance matrix , and  represents the error term. The Gaussian process is defined as:

where  is the variance set to 1e-6. During inference, we update the pseudotime by maximizing the posterior liklihood. Specifically, the posterior distribution of pseudotime is obtained by combining the observed data Y* with the prior distribution of the Gaussian process model.

We use the Markov Chain Monte Carlo method for parameter estimation, particularly employing the adaptive Metropolis-within-Gibbs (AMWG) sampling to handle the high autocorrelation of pseudotime.

Comment#8: Enhancing Readability: For clarity, provide intuitive descriptions of each evaluation function used in simulated and real data. The novel methodology performs well for some metrics but less so for others. A clear understanding of these measurements is essential.

To address the concern of readability, we have added descriptions of 5 evaluation metrics in the methodology section (Benchmarking MGPfact to state-of-the-art methods) in line 494 to 515. Additionally, we have included a summary and discussion of these metrics in the conclusion section in line 214-240 to help the readers better understand the significance and impact of these measurements.

(1) In brief, the Hamming-Ipsen-Mikhailov ( ) distance measures the similarity between topological structures, combining the normalized Hamming distance and the Ipsen-Mikhailov distance, which focus on edge length differences and degree distribution similarity, respectively. The  is used to assess the accuracy of a model's branch assignment via Jaccard similarity between branch pairs. In trajectory inference, cor_dist quantifies the similarity of inter-cell distances between predicted and true trajectories, evaluating the accuracy of cell ordering. The wcor_features assesses the similarity of key features through weighted Pearson correlation, capturing biological variation. The score is calculated as the geometric mean of these metrics, providing an assessment of overall performance.

(2) For MGPfact and the other seven methods included in the comparison, each has its own focus. MGPfact specializes in factorizing complex cell trajectories using Gaussian process mixture models, making it particularly capable of identifying bifurcation events. Therefore, it excels in the accuracy of branch partitioning and similarity of trajectory topology. Among other methods, scShaper (Smolander et al., 2022) and TSCAN(Ji and Ji, 2016) are more suited for generating linear trajectories and excel in linear datasets, accurately predicting pseudotime. The Monocle series, as typical representatives of tree methods, effectively capture complex topologies and are suitable for analyzing cell data with diversified differentiation paths.

Comment#9: Microglia Analysis:In Figures 3A-C, the genes mentioned in the text for each bifurcation do not always match those shown in the panels. Please confirm this.

Thank you for pointing this out. We have carefully reviewed the article and corrected the error where the genes shown in the figures did not correspond to the descriptions in the article. The specific corrections have been made between line 257 and 264:

The first bifurcation determines the differentiated cell fates of PAM and HM, which involves a set of notable marker genes of both cell types, such as Apoe, Selplg (HM), and Gpnmb (PAM). The second bifurcation determines the proliferative status, which is crucial for the development and function of PAM and HM (Guzmán, n.d.; Li et al., 2019). The genes affected by the second bifurcation are associated with cell cycle and proliferation, such as Mki67, Tubb5, Top2a. The third bifurcation influences the development and maturity of microglia, of which the highly weighted genes, such as Tmem119, P2ry12, and Sepp1 are all previously annotated markers for establishment of the fates of microglia (Anderson et al., 2022; Li et al., 2019) (Supplementary Table 4).

Comment#10: Regulons:

- The conclusions rely heavily on regulons. The Methods section describes using SCENIC, GENIE3, RCisTarget, and AUCell, but their relation to bifurcation analysis is unclear.

- Do you perform trajectory analysis on all MURP-derived cells or within each identified trajectory based on bifurcation? This point needs clarification to make the outcomes comprehensible. The legend of Figure 4 provides some ideas, but further clarity is required.

Thank you for the comments.

(1) To clarify, we used the tools like SCENIC to annotate the highly weighted genes (HWG) resulted from the bifurcation analysis for transcription factor regulation activity and possible impacts on biological processes. We have added descriptions to the analysis of our microglial data. The revised content is between line 265 and 266:

Moreover, we retrieved highly active regulons from the HWG by MGPfact, of which the significance is quantified by the overall weights of the member genes.

(2) We apologize for any confusion caused by our description. It is important to clarify that we performed an overall trajectory analysis on all MURP results, rather than analyzing within each identified trajectory. Specifically, we first used MURP to downsample all preprocessed cells, where each MURP subset represents a group of cells. We then conducted trajectory inference on all MURP subsets and identified bifurcation points. This process generated multiple independent differentiation trajectories, encompassing all MURP subsets. To clearly convey this point, we have added descriptions in the legend of Figure 4. The revised content is between line 276 and 283:

“Fig. 4. MGPfact reconstructed the developmental trajectory of microglia, recovering known determinants of microglia fate. a-c. The inferred independent bifurcation processes with respect to the unique cell types (color-coded) of microglia development, where phase 0 corresponds to the state before bifurcation; and phases 1 and 2 correspond to the states post-bifurcation. Each colored dot represents a metacell of unique cell type defined by MURP. The most highly weighted regulons in each trajectory were labeled by the corresponding transcription factors (left panels). The HWG of each bifurcation process include a set of highly weighted genes (HWG), of which the expression levels differ significantly among phases 1, 2, and 3 (right panels).”

Comment#11: CD8+ T Cells: The comparison is made against Monocle2, the method used in the publication, but it would be beneficial to compare it with more recent methods. Otherwise, the added value of MGPfact is unclear.

Per your request, we have expanded our comparative analysis to include not only Monocle2 but also more recent methods such as Monocle3 (Cao et al., 2019) and scFates Tree (Faure et al., 2023). We used adjusted R-squared values to evaluate each method's ability to explain trajectory variation. The results have been added to Table 2 and Supplementary Table 6. The revised content is between line 318 and 326:

We assessed the goodness-of-fit (adjusted R-square) of the consensus trajectory derived by MGPfact and three methods (Monocle 2, Monocle 3 and scFates Tree) for the CD8+ T cell subtypes described in the original studies (Guo et al., 2018; Zhang et al., 2018). The data showed that MGPfact significantly improved the explanatory power for most CD8+ T cell subtypes over Monocle 2, which was used in the original studies (P < 0.05, see Table 2 and Supplementary Table 6), except for the CD8-GZMK cells in the CRC dataset. Additionally, MGPfact demonstrated better explanatory power in specific cell types when compared to Monocle 3 and scFates Tree. For instance, in the NSCLC dataset, MGPfact exhibited higher explanatory power for CD8-LEF1 cells (Table 2, R-squared = 0.935), while Monocle 3 and scFates Tree perform better in other cell types.

Comment#12: Consensus Trajectory: A panel explaining how the consensus trajectory is generated would be helpful. Include both visual and textual explanations tailored to the journal's audience.

Thank you for the comments. Regarding how the consensus trajectory is constructed, we have illustrated and described this in Figure 1 and the supplementary methods. Taking the reviewers' suggestions into account, we have added more details about the generation process of the consensus trajectory in the methods section to enhance the completeness of the manuscript. The revised content is from line 599 to 606:

Following MGPfact decomposition, we obtained multiple independent bifurcative trajectories, each corresponds to a binary tree within the temporal domain. These trajectories were then merged to construct a coherent diffusion tree, representing the consensus trajectory of cells’ fate. The merging process involves initially sorting all trajectories by their bifurcation time. The first (earliest) bifurcative trajectory is chosen as the initial framework, and subsequent trajectories are integrated to the initial framework iteratively by adding the corresponding branches at the bifurcation timepoints. As a result, the trajectories are ultimately merged into a comprehensive binary tree, serving as the consensus trajectory.

Comment#13: Discussion:

- Check for typos, e.g., line 382 "pseudtime.".

- Avoid considering HVG as the entire feature space.

- The first three paragraphs are too similar to the Introduction. Consider shortening them to succinctly state the scenario and the implications of your contribution.

Thank you for pointing out the typos.

(1) We conducted a comprehensive review of the document to ensure there are no typographical errors.

(2) We restructured the first three paragraphs of the discussion section to clarify the limitations in the use of current manifold-learning methods and removed any absolute language regarding treating HVGs as the entire feature space. The revised content is from line 419 to 430:

Single-cell RNA sequencing (scRNA-seq) provides a direct, quantitative snapshot of a population of cells in certain biological conditions, thereby revealing the actual cell states and functions. Although existing clustering and embedding algorithms can effectively reveal discrete biological states of cells, these methods become less efficient when depicting continuous evolving of cells over the temporal domain. The introduction of manifold learning offers a new dimension for discovery of relevant biological knowledge in cell fate determination, allowing for a better representation of continuous changes in cells, especially in time-dependent processes such as development, differentiation, and clonal evolution. However, current manifold learning methods face major limitations, such as the need for prior information on pseudotime and cell clustering, and lack of explainability, which restricts their applicability. Additionally, many existing trajectory inference methods do not support gene selection, making it difficult to annotate the results to known biological entities, thereby hindering the interpretation of results and subsequent functional studies.

Comment#14: Minor Comments:

(1) Review the paragraph regarding the "current manifold-learning methods are faced with two major challenges." The message needs clarification.

(2) Increase the quality of the figures.

(3) Update the numbering of equations from #(.x) to (x).

We thank the reviewer for these detailed suggestions.

(1) We have thoroughly revised the discussion section, addressing overly absolute statements. The revised content is from line 426 to 428:

However, current manifold learning methods face major limitations, such as the need for prior information on pseudotime and cell clustering, and lack of explainability, which restricts their applicability.

(2) We conducted a comprehensive review of the figures in the article to more clearly present our results.

(3) We have meticulously reviewed the equations in the article to ensure there are no display issues with the indices.

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